In [75]:
library(data.table)
library(clusterProfiler)
library(dplyr)
library(Seurat)
library(ggplot2)
library(circlize)
In [76]:
library(org.Hs.eg.db)
library(msigdbr)
Loading required package: AnnotationDbi
Loading required package: stats4
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Attaching package: ‘BiocGenerics’
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match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
table, tapply, union, unique, unsplit, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
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Attaching package: ‘S4Vectors’
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In [77]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, entrez_gene)
head(m_t2g)
| gs_name | entrez_gene |
|---|---|
| <chr> | <int> |
| HALLMARK_ADIPOGENESIS | 19 |
| HALLMARK_ADIPOGENESIS | 11194 |
| HALLMARK_ADIPOGENESIS | 10449 |
| HALLMARK_ADIPOGENESIS | 33 |
| HALLMARK_ADIPOGENESIS | 34 |
| HALLMARK_ADIPOGENESIS | 35 |
In [3]:
fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/all_driver_types_match_counts.csv')
| seed | shap_total | shap_0 | shap_1 | grad_total | grad_0 | grad_1 |
|---|---|---|---|---|---|---|
| <int> | <int> | <int> | <int> | <int> | <int> | <int> |
| 0 | 25 | 25 | 25 | 22 | 24 | 23 |
| 1 | 24 | 26 | 25 | 23 | 24 | 24 |
| 2 | 24 | 23 | 25 | 20 | 24 | 18 |
| 3 | 24 | 25 | 25 | 25 | 24 | 25 |
| 4 | 24 | 24 | 26 | 22 | 20 | 23 |
| 5 | 24 | 24 | 26 | 20 | 24 | 18 |
| 6 | 24 | 24 | 24 | 22 | 24 | 23 |
| 7 | 24 | 24 | 26 | 22 | 26 | 23 |
| 8 | 22 | 23 | 23 | 23 | 25 | 24 |
| 9 | 23 | 23 | 25 | 23 | 23 | 24 |
In [4]:
fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/driver_summary_shap_total.csv')
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| FN1 | 0.007993378 | 0.0007537607 | 1.907695e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| HIST1H2BD | 0.003864827 | 0.0005008594 | 1.813786e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.002306744 | 0.0002728280 | 1.020445e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| KRT8 | 0.004171373 | 0.0006077977 | 7.349687e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.006570227 | 0.0004776801 | 1.470234e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD9 | 0.004286982 | 0.0004431204 | 1.179431e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE |
| LAPTM4A | 0.005557391 | 0.0006989103 | 1.191966e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| MDK | 0.004485494 | 0.0007073663 | 9.334373e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| PRDX1 | 0.005753293 | 0.0005948305 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE |
| PGK1 | 0.002937007 | 0.0004105406 | 8.659618e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| NQO1 | 0.007189172 | 0.0005834166 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE |
| ALPP | 0.003790364 | 0.0003220504 | 1.560096e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| MT2A | 0.002992815 | 0.0007070334 | -5.982608e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| PKM | 0.002418476 | 0.0004418046 | 8.013423e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| UBB | 0.002093029 | 0.0004443382 | -5.899295e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| FOSL1 | 0.004265769 | 0.0005058976 | -9.429040e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| CTSA | 0.004027971 | 0.0005851937 | 1.295741e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| UBE2S | 0.004784057 | 0.0005292148 | -1.516910e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| HSPB1 | 0.002017296 | 0.0003566292 | 7.373099e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| MYL6 | 0.008508458 | 0.0008462955 | 1.682113e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TCOF1 | 0.002286529 | 0.0003146553 | -6.928714e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| TFDP1 | 0.003330428 | 0.0003013794 | -9.927206e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| CSTB | 0.003148289 | 0.0003750370 | -1.198390e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| JUND | 0.001684324 | 0.0003056773 | 1.057126e-04 | 10 | TRUE | FALSE | FALSE | FALSE |
| TGM2 | 0.005329582 | 0.0004317851 | -1.178054e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| SQSTM1 | 0.002869604 | 0.0004758771 | 9.440278e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| HIST1H4C | 0.003751588 | 0.0004830967 | -1.076414e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CKS1B | 0.002607002 | 0.0003412548 | 9.403953e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| CD24 | 0.002727851 | 0.0002715580 | 1.166907e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| CENPN | 0.0021267512 | 3.911722e-04 | 8.123492e-05 | 8 | FALSE | FALSE | FALSE | FALSE |
| HMGA1 | 0.0012568032 | 5.449500e-04 | -4.842230e-05 | 7 | TRUE | FALSE | FALSE | FALSE |
| CD81 | 0.0026813797 | 2.755875e-04 | 1.560641e-04 | 7 | FALSE | FALSE | FALSE | FALSE |
| PPIF | 0.0013978999 | 1.616978e-04 | -1.086447e-05 | 6 | FALSE | FALSE | FALSE | FALSE |
| PSMB6 | 0.0015816240 | 1.423927e-04 | 9.055104e-05 | 5 | FALSE | FALSE | FALSE | FALSE |
| CDK1 | 0.0007892986 | 1.556827e-04 | -6.600696e-05 | 5 | FALSE | FALSE | FALSE | FALSE |
| CCNB1 | 0.0009600252 | 7.355914e-05 | 3.018131e-07 | 5 | FALSE | FALSE | FALSE | FALSE |
| SERPINH1 | 0.0011331889 | 2.452786e-04 | 5.132065e-05 | 5 | FALSE | FALSE | FALSE | FALSE |
| FOXM1 | 0.0006686676 | 1.551108e-04 | -7.297643e-06 | 5 | TRUE | FALSE | FALSE | FALSE |
| LAMP1 | 0.0014675947 | 3.839450e-04 | 7.594953e-05 | 5 | FALSE | FALSE | FALSE | FALSE |
| PPP1R14B | 0.0011937528 | 1.994650e-04 | -8.283027e-06 | 4 | FALSE | FALSE | FALSE | FALSE |
| TFPI2 | 0.0011605380 | 2.944522e-04 | 7.088958e-05 | 4 | FALSE | FALSE | FALSE | FALSE |
| ALDH3A1 | 0.0013055377 | 1.966169e-04 | 4.354592e-07 | 4 | FALSE | TRUE | FALSE | TRUE |
| CALD1 | 0.0006721359 | 9.202680e-06 | 3.342999e-07 | 3 | FALSE | FALSE | FALSE | FALSE |
| TUBA4A | 0.0005829521 | 1.696022e-04 | 1.596368e-07 | 3 | FALSE | FALSE | FALSE | FALSE |
| IGFBP3 | 0.0010258654 | 2.486552e-04 | 1.015227e-05 | 3 | FALSE | FALSE | FALSE | FALSE |
| TUBB4B | 0.0003830904 | 4.519722e-05 | 7.412989e-08 | 2 | FALSE | FALSE | FALSE | FALSE |
| MYBL2 | 0.0002402041 | 1.968738e-04 | 6.863686e-06 | 2 | TRUE | FALSE | FALSE | FALSE |
| PHGDH | 0.0004173670 | 5.805091e-05 | 2.442747e-07 | 2 | FALSE | FALSE | FALSE | FALSE |
| UBC | 0.0002102067 | 6.868151e-05 | 1.274175e-07 | 2 | FALSE | FALSE | FALSE | FALSE |
| PCNA | 0.0004494411 | 1.240919e-04 | 5.007451e-05 | 2 | FALSE | FALSE | FALSE | FALSE |
| HIST1H1C | 0.0004837657 | 1.441257e-04 | 9.379254e-06 | 2 | FALSE | FALSE | FALSE | FALSE |
| CTNNB1 | 0.0001381328 | 0.000000e+00 | -9.900118e-09 | 1 | FALSE | FALSE | FALSE | FALSE |
| CALR | 0.0002454971 | 0.000000e+00 | -1.203399e-07 | 1 | FALSE | FALSE | FALSE | FALSE |
| MCM3 | 0.0001789002 | 0.000000e+00 | 2.958242e-10 | 1 | FALSE | FALSE | FALSE | FALSE |
| CENPF | 0.0002376977 | 0.000000e+00 | 6.111640e-09 | 1 | FALSE | FALSE | FALSE | FALSE |
| ANXA2 | 0.0001817979 | 0.000000e+00 | -2.258579e-10 | 1 | FALSE | FALSE | FALSE | FALSE |
| TOP2A | 0.0002693551 | 0.000000e+00 | -8.096725e-10 | 1 | FALSE | FALSE | FALSE | FALSE |
| NDC80 | 0.0002256619 | 0.000000e+00 | -6.941362e-08 | 1 | FALSE | FALSE | FALSE | FALSE |
| SMC4 | 0.0001939561 | 0.000000e+00 | -6.988958e-08 | 1 | FALSE | FALSE | FALSE | FALSE |
In [5]:
fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/driver_summary_grad_total.csv')
| V1 | weight_grad_total_mean | weight_grad_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| FN1 | 0.0033195013 | 0.0006092176 | 1.907695e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TGM2 | 0.0024723618 | 0.0002408337 | -1.178054e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| FOSL1 | 0.0021386624 | 0.0003259176 | -9.429040e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| TUBA1A | 0.0014271371 | 0.0003019460 | -6.416579e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| NQO1 | 0.0031087001 | 0.0004006423 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE |
| PRDX1 | 0.0027994787 | 0.0002450953 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE |
| HIST1H2BD | 0.0025927966 | 0.0007508685 | 1.813786e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| MDK | 0.0022288554 | 0.0003639519 | 9.334373e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| LAPTM4A | 0.0021388518 | 0.0002744455 | 1.191966e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| H2AFZ | 0.0010318836 | 0.0003560341 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE |
| GSTM3 | 0.0025723606 | 0.0002970675 | 1.470234e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| KRT8 | 0.0017838659 | 0.0003099043 | 7.349687e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.0018784931 | 0.0003017914 | 1.020445e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| UBE2S | 0.0024224087 | 0.0004112686 | -1.516910e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| HIST1H4C | 0.0019507386 | 0.0004960266 | -1.076414e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| SQSTM1 | 0.0016622826 | 0.0003259419 | 9.440278e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| MYL6 | 0.0034069135 | 0.0003737256 | 1.682113e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| JUND | 0.0019297022 | 0.0004503811 | 1.057126e-04 | 10 | TRUE | FALSE | FALSE | FALSE |
| TFDP1 | 0.0020451669 | 0.0002417682 | -9.927206e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| CSTB | 0.0019238865 | 0.0002683021 | -1.198390e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TCOF1 | 0.0014676890 | 0.0003327820 | -6.928714e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD9 | 0.0018183152 | 0.0002345082 | 1.179351e-04 | 9 | FALSE | FALSE | FALSE | FALSE |
| PKM | 0.0013774977 | 0.0002139187 | 8.007810e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| PPIF | 0.0015920478 | 0.0003511012 | -1.100813e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| LDHB | 0.0013097703 | 0.0002789830 | 6.335115e-05 | 8 | FALSE | FALSE | FALSE | FALSE |
| CD81 | 0.0023239044 | 0.0002641056 | 1.738500e-04 | 8 | FALSE | FALSE | FALSE | FALSE |
| ALPP | 0.0020465455 | 0.0003523491 | 1.559023e-04 | 8 | FALSE | FALSE | FALSE | FALSE |
| CDK1 | 0.0010983661 | 0.0002357872 | -6.599018e-05 | 8 | FALSE | FALSE | FALSE | FALSE |
| FOXM1 | 0.0010164819 | 0.0003322221 | -4.373933e-05 | 8 | TRUE | FALSE | FALSE | FALSE |
| ATF3 | 0.0007296726 | 0.0001532419 | 4.546158e-05 | 7 | TRUE | FALSE | FALSE | FALSE |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| CTNNB1 | 6.839809e-04 | 2.792041e-04 | -3.814569e-05 | 6 | FALSE | FALSE | FALSE | FALSE |
| CTSA | 1.311533e-03 | 2.833791e-04 | 1.293621e-04 | 6 | FALSE | FALSE | FALSE | FALSE |
| TUBA1B | 6.988455e-04 | 1.660443e-04 | -2.186076e-07 | 5 | FALSE | FALSE | FALSE | FALSE |
| KLF4 | 6.236043e-04 | 3.061658e-04 | -2.170733e-07 | 5 | TRUE | FALSE | FALSE | FALSE |
| CALR | 6.278391e-04 | 4.215074e-04 | -4.846227e-07 | 4 | FALSE | FALSE | FALSE | FALSE |
| UBB | 4.775177e-04 | 1.961124e-04 | -5.885883e-05 | 4 | FALSE | FALSE | FALSE | FALSE |
| GATA2 | 3.396291e-04 | 1.058573e-04 | 4.667523e-05 | 4 | TRUE | FALSE | FALSE | FALSE |
| NDC80 | 7.728954e-04 | 1.148955e-04 | -2.418145e-07 | 4 | FALSE | FALSE | FALSE | FALSE |
| NME1 | 7.228874e-04 | 2.816583e-04 | 4.719073e-07 | 4 | FALSE | FALSE | FALSE | FALSE |
| GABARAPL1 | 3.815320e-04 | 7.253918e-05 | 6.670668e-05 | 3 | FALSE | TRUE | FALSE | TRUE |
| TUBA4A | 3.588999e-04 | 1.403488e-04 | 1.846510e-07 | 3 | FALSE | FALSE | FALSE | FALSE |
| HMGA2 | 5.102370e-04 | 1.755273e-04 | -1.525581e-07 | 3 | TRUE | FALSE | FALSE | FALSE |
| SMC4 | 2.576033e-04 | 5.121550e-05 | -1.792321e-07 | 2 | FALSE | FALSE | FALSE | FALSE |
| AURKB | 2.642782e-04 | 6.619854e-05 | -1.687990e-08 | 2 | FALSE | FALSE | FALSE | FALSE |
| HSPA1A | 2.431559e-04 | 2.353959e-05 | -1.418870e-08 | 2 | FALSE | FALSE | FALSE | FALSE |
| CENPN | 3.170966e-04 | 3.055441e-05 | 7.491441e-05 | 2 | FALSE | FALSE | FALSE | FALSE |
| AURKA | 2.958436e-04 | 2.257436e-04 | -5.137581e-08 | 2 | FALSE | FALSE | FALSE | FALSE |
| EGR1 | 2.701469e-04 | 1.846625e-04 | -1.105947e-08 | 2 | TRUE | FALSE | FALSE | FALSE |
| HMGA1 | 1.855564e-04 | 4.237684e-05 | -4.394165e-05 | 2 | TRUE | FALSE | FALSE | FALSE |
| APP | 1.230560e-04 | 0.000000e+00 | 7.437055e-06 | 1 | FALSE | FALSE | FALSE | FALSE |
| MYBL2 | 8.287637e-05 | 0.000000e+00 | -5.011473e-09 | 1 | TRUE | FALSE | FALSE | FALSE |
| CCNA2 | 1.374829e-04 | 0.000000e+00 | -7.023238e-09 | 1 | FALSE | FALSE | FALSE | FALSE |
| CEBPB | 7.743918e-05 | 0.000000e+00 | -1.706378e-08 | 1 | TRUE | FALSE | FALSE | FALSE |
| GTSE1 | 1.651362e-04 | 0.000000e+00 | -9.854612e-09 | 1 | FALSE | FALSE | FALSE | FALSE |
| LAMP1 | 1.789470e-04 | 0.000000e+00 | 9.990827e-08 | 1 | FALSE | FALSE | FALSE | FALSE |
| SERPINH1 | 1.432284e-04 | 0.000000e+00 | 5.493789e-08 | 1 | FALSE | FALSE | FALSE | FALSE |
| JUNB | 1.218797e-04 | 0.000000e+00 | 1.201996e-07 | 1 | TRUE | FALSE | TRUE | TRUE |
| CCNB1 | 1.349889e-04 | 0.000000e+00 | 2.435022e-07 | 1 | FALSE | FALSE | FALSE | FALSE |
| CALD1 | 1.464856e-04 | 0.000000e+00 | 8.800959e-09 | 1 | FALSE | FALSE | FALSE | FALSE |
| GSN | 1.579424e-04 | 0.000000e+00 | -1.304140e-07 | 1 | FALSE | FALSE | FALSE | FALSE |
In [151]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [152]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| FN1 | 0.007993378 | 0.0007537607 | 1.907695e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| HIST1H2BD | 0.003864827 | 0.0005008594 | 1.813786e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.002306744 | 0.0002728280 | 1.020445e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| KRT8 | 0.004171373 | 0.0006077977 | 7.349687e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.006570227 | 0.0004776801 | 1.470234e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD9 | 0.004286982 | 0.0004431204 | 1.179431e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
In [9]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("UNIPROT","ENTREZID"), OrgDb="org.Hs.eg.db")
'select()' returned 1:many mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("UNIPROT", "ENTREZID"), :
“6.35% of input gene IDs are fail to map...”
In [10]:
go <- enrichGO(gene = ,
OrgDb = org.Hs.eg.db,
keyType = 'ENTREZID',
ont = "BP",
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
In [11]:
go@result
| ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| GO:0000280 | GO:0000280 | nuclear division | 11/58 | 441/18870 | 8.497327e-08 | 0.0001724108 | 0.0001122542 | 7314/27338/5902/983/891/4605/811/1063/7153/10403/10051 | 11 |
| GO:0048285 | GO:0048285 | organelle fission | 11/58 | 488/18870 | 2.349718e-07 | 0.0002383788 | 0.0001552050 | 7314/27338/5902/983/891/4605/811/1063/7153/10403/10051 | 11 |
| GO:0009410 | GO:0009410 | response to xenobiotic stimulus | 10/58 | 434/18870 | 7.265422e-07 | 0.0003730989 | 0.0002429193 | 595/2947/1728/8061/983/218/5111/1499/811/1063 | 10 |
| GO:0045787 | GO:0045787 | positive regulation of cell cycle | 9/58 | 334/18870 | 7.603798e-07 | 0.0003730989 | 0.0002429193 | 595/8061/7027/5902/983/891/811/10403/10051 | 9 |
| GO:0048144 | GO:0048144 | fibroblast proliferation | 6/58 | 107/18870 | 9.194157e-07 | 0.0003730989 | 0.0002429193 | 2335/5052/1163/983/891/1499 | 6 |
| GO:0140014 | GO:0140014 | mitotic nuclear division | 8/58 | 274/18870 | 1.824414e-06 | 0.0006169560 | 0.0004016911 | 27338/5902/983/891/4605/1063/10403/10051 | 8 |
| GO:0000302 | GO:0000302 | response to reactive oxygen species | 7/58 | 205/18870 | 3.031463e-06 | 0.0008786912 | 0.0005721032 | 5052/1728/8061/8878/10105/983/5111 | 7 |
| GO:0000819 | GO:0000819 | sister chromatid segregation | 7/58 | 225/18870 | 5.598675e-06 | 0.0014199639 | 0.0009245180 | 983/891/4605/1063/7153/10403/10051 | 7 |
| GO:0034614 | GO:0034614 | cellular response to reactive oxygen species | 6/58 | 154/18870 | 7.642157e-06 | 0.0016092253 | 0.0010477434 | 5052/1728/8878/10105/983/5111 | 6 |
| GO:0044839 | GO:0044839 | cell cycle G2/M phase transition | 6/58 | 155/18870 | 7.931125e-06 | 0.0016092253 | 0.0010477434 | 595/983/891/2305/1063/10403 | 6 |
| GO:0033044 | GO:0033044 | regulation of chromosome organization | 7/58 | 247/18870 | 1.029670e-05 | 0.0017884238 | 0.0011644169 | 983/891/1499/1063/7153/10403/10051 | 7 |
| GO:0007292 | GO:0007292 | female gamete generation | 6/58 | 163/18870 | 1.057717e-05 | 0.0017884238 | 0.0011644169 | 4192/7314/8061/1499/7153/10403 | 6 |
| GO:0044772 | GO:0044772 | mitotic cell cycle phase transition | 9/58 | 470/18870 | 1.231576e-05 | 0.0019133104 | 0.0012457288 | 595/27338/7027/1163/983/891/2305/1063/10403 | 9 |
| GO:0010038 | GO:0010038 | response to metal ion | 8/58 | 359/18870 | 1.327532e-05 | 0.0019133104 | 0.0012457288 | 1728/4502/3727/7846/10105/983/5111/811 | 8 |
| GO:0042542 | GO:0042542 | response to hydrogen peroxide | 5/58 | 101/18870 | 1.454701e-05 | 0.0019133104 | 0.0012457288 | 1728/8061/10105/983/5111 | 5 |
| GO:0090068 | GO:0090068 | positive regulation of cell cycle process | 7/58 | 262/18870 | 1.508771e-05 | 0.0019133104 | 0.0012457288 | 595/7027/5902/983/891/10403/10051 | 7 |
| GO:0000070 | GO:0000070 | mitotic sister chromatid segregation | 6/58 | 184/18870 | 2.105023e-05 | 0.0024196270 | 0.0015753843 | 983/891/4605/1063/10403/10051 | 6 |
| GO:0051402 | GO:0051402 | neuron apoptotic process | 7/58 | 278/18870 | 2.209923e-05 | 0.0024196270 | 0.0015753843 | 595/4192/1728/7314/7846/4605/1499 | 7 |
| GO:0033047 | GO:0033047 | regulation of mitotic sister chromatid segregation | 4/58 | 54/18870 | 2.265792e-05 | 0.0024196270 | 0.0015753843 | 983/891/1063/10403 | 4 |
| GO:0031099 | GO:0031099 | regeneration | 6/58 | 191/18870 | 2.598302e-05 | 0.0026359771 | 0.0017162466 | 595/928/4192/975/983/5111 | 6 |
| GO:0071248 | GO:0071248 | cellular response to metal ion | 6/58 | 200/18870 | 3.365097e-05 | 0.0032498273 | 0.0021159157 | 1728/4502/3727/7846/10105/811 | 6 |
| GO:0051383 | GO:0051383 | kinetochore organization | 3/58 | 21/18870 | 3.523716e-05 | 0.0032498273 | 0.0021159157 | 1063/10403/10051 | 3 |
| GO:0007059 | GO:0007059 | chromosome segregation | 8/58 | 424/18870 | 4.360081e-05 | 0.0038463500 | 0.0025043030 | 55839/983/891/4605/1063/7153/10403/10051 | 8 |
| GO:0098813 | GO:0098813 | nuclear chromosome segregation | 7/58 | 312/18870 | 4.610380e-05 | 0.0038976922 | 0.0025377312 | 983/891/4605/1063/7153/10403/10051 | 7 |
| GO:0051983 | GO:0051983 | regulation of chromosome segregation | 5/58 | 131/18870 | 5.095420e-05 | 0.0041354431 | 0.0026925273 | 983/891/1063/10403/10051 | 5 |
| GO:0070301 | GO:0070301 | cellular response to hydrogen peroxide | 4/58 | 67/18870 | 5.330706e-05 | 0.0041600010 | 0.0027085167 | 1728/10105/983/5111 | 4 |
| GO:1905448 | GO:1905448 | positive regulation of mitochondrial ATP synthesis coupled electron transport | 2/58 | 4/18870 | 5.548980e-05 | 0.0041699556 | 0.0027149980 | 983/891 | 2 |
| GO:0007098 | GO:0007098 | centrosome cycle | 5/58 | 138/18870 | 6.528838e-05 | 0.0046997710 | 0.0030599531 | 7846/5902/983/1499/10403 | 5 |
| GO:0000086 | GO:0000086 | G2/M transition of mitotic cell cycle | 5/58 | 140/18870 | 6.990543e-05 | 0.0046997710 | 0.0030599531 | 595/983/891/2305/1063 | 5 |
| GO:0071241 | GO:0071241 | cellular response to inorganic substance | 6/58 | 229/18870 | 7.149451e-05 | 0.0046997710 | 0.0030599531 | 1728/4502/3727/7846/10105/811 | 6 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| GO:0043010 | GO:0043010 | camera-type eye development | 1/58 | 344/18870 | 0.6565542 | 0.6660743 | 0.4336713 | 1499 | 1 |
| GO:0034249 | GO:0034249 | negative regulation of amide metabolic process | 1/58 | 345/18870 | 0.6576295 | 0.6668317 | 0.4341645 | 811 | 1 |
| GO:0071496 | GO:0071496 | cellular response to external stimulus | 1/58 | 346/18870 | 0.6587014 | 0.6675850 | 0.4346549 | 8061 | 1 |
| GO:0032496 | GO:0032496 | response to lipopolysaccharide | 1/58 | 348/18870 | 0.6608354 | 0.6694134 | 0.4358454 | 1728 | 1 |
| GO:0045862 | GO:0045862 | positive regulation of proteolysis | 1/58 | 350/18870 | 0.6629562 | 0.6708919 | 0.4368080 | 302 | 1 |
| GO:0051235 | GO:0051235 | maintenance of location | 1/58 | 350/18870 | 0.6629562 | 0.6708919 | 0.4368080 | 811 | 1 |
| GO:0070588 | GO:0070588 | calcium ion transmembrane transport | 1/58 | 352/18870 | 0.6650640 | 0.6726894 | 0.4379784 | 302 | 1 |
| GO:0030522 | GO:0030522 | intracellular receptor signaling pathway | 1/58 | 353/18870 | 0.6661131 | 0.6734148 | 0.4384507 | 811 | 1 |
| GO:0090287 | GO:0090287 | regulation of cellular response to growth factor stimulus | 1/58 | 357/18870 | 0.6702771 | 0.6772870 | 0.4409718 | 1499 | 1 |
| GO:0009100 | GO:0009100 | glycoprotein metabolic process | 1/58 | 370/18870 | 0.6834607 | 0.6899213 | 0.4491978 | 1499 | 1 |
| GO:0032535 | GO:0032535 | regulation of cellular component size | 1/58 | 370/18870 | 0.6834607 | 0.6899213 | 0.4491978 | 2335 | 1 |
| GO:1901653 | GO:1901653 | cellular response to peptide | 1/58 | 374/18870 | 0.6874120 | 0.6935649 | 0.4515701 | 5315 | 1 |
| GO:0002460 | GO:0002460 | adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains | 1/58 | 380/18870 | 0.6932482 | 0.6991056 | 0.4551776 | 975 | 1 |
| GO:0001654 | GO:0001654 | eye development | 1/58 | 391/18870 | 0.7036710 | 0.7092640 | 0.4617916 | 1499 | 1 |
| GO:0150063 | GO:0150063 | visual system development | 1/58 | 395/18870 | 0.7073742 | 0.7126426 | 0.4639913 | 1499 | 1 |
| GO:0031331 | GO:0031331 | positive regulation of cellular catabolic process | 1/58 | 396/18870 | 0.7082928 | 0.7132140 | 0.4643633 | 8878 | 1 |
| GO:0010959 | GO:0010959 | regulation of metal ion transport | 1/58 | 398/18870 | 0.7101217 | 0.7147008 | 0.4653314 | 1499 | 1 |
| GO:0048880 | GO:0048880 | sensory system development | 1/58 | 401/18870 | 0.7128438 | 0.7170848 | 0.4668836 | 1499 | 1 |
| GO:0003002 | GO:0003002 | regionalization | 1/58 | 430/18870 | 0.7378958 | 0.7415506 | 0.4828129 | 1499 | 1 |
| GO:0043434 | GO:0043434 | response to peptide hormone | 1/58 | 430/18870 | 0.7378958 | 0.7415506 | 0.4828129 | 5315 | 1 |
| GO:0042391 | GO:0042391 | regulation of membrane potential | 1/58 | 440/18870 | 0.7460261 | 0.7493500 | 0.4878910 | 7314 | 1 |
| GO:0007409 | GO:0007409 | axonogenesis | 1/58 | 448/18870 | 0.7523514 | 0.7553296 | 0.4917842 | 2335 | 1 |
| GO:0006869 | GO:0006869 | lipid transport | 1/58 | 453/18870 | 0.7562259 | 0.7588439 | 0.4940723 | 302 | 1 |
| GO:0034765 | GO:0034765 | regulation of monoatomic ion transmembrane transport | 1/58 | 454/18870 | 0.7569936 | 0.7592387 | 0.4943294 | 10105 | 1 |
| GO:0007015 | GO:0007015 | actin filament organization | 1/58 | 464/18870 | 0.7645412 | 0.7664299 | 0.4990114 | 800 | 1 |
| GO:0007389 | GO:0007389 | pattern specification process | 1/58 | 475/18870 | 0.7725776 | 0.7741036 | 0.5040077 | 1499 | 1 |
| GO:0023061 | GO:0023061 | signal release | 1/58 | 484/18870 | 0.7789518 | 0.7801052 | 0.5079152 | 7052 | 1 |
| GO:0050804 | GO:0050804 | modulation of chemical synaptic transmission | 1/58 | 489/18870 | 0.7824168 | 0.7831888 | 0.5099229 | 8878 | 1 |
| GO:0099177 | GO:0099177 | regulation of trans-synaptic signaling | 1/58 | 490/18870 | 0.7831033 | 0.7834895 | 0.5101187 | 8878 | 1 |
| GO:0031667 | GO:0031667 | response to nutrient levels | 1/58 | 495/18870 | 0.7865044 | 0.7865044 | 0.5120817 | 1728 | 1 |
In [33]:
ggo <- groupGO(gene = gene$ENTREZID,
OrgDb = org.Hs.eg.db,
keyType = 'ENTREZID',
ont = "BP",
#level = 3,
readable = TRUE)
In [34]:
ggo@result
| ID | Description | Count | GeneRatio | geneID | |
|---|---|---|---|---|---|
| <chr> | <chr> | <int> | <chr> | <chr> | |
| GO:0000003 | GO:0000003 | reproduction | 29 | 29/59 | KRT8/KRT8/CD9/CD9/CD9/MDK/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CDK1/CDK1/CDK1/CDK1/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4 |
| GO:0002376 | GO:0002376 | immune system process | 33 | 33/59 | FN1/FN1/FN1/FN1/FN1/FN1/CD9/CD9/CD9/MDK/PRDX1/NQO1/NQO1/FOSL1/FOSL1/FOSL1/FOSL1/SQSTM1/CD24/CD24/CD24/CD81/CD81/CD81/LAMP1/LAMP1/PPP1R14B/TUBB4B/CTNNB1/CTNNB1/CTNNB1/CALR/CALR |
| GO:0008152 | GO:0008152 | metabolic process | 100 | 100/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/GSTM3/GSTM3/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/ALPP/ALPP/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/UBE2S/HSPB1/HSPB1/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/CSTB/CSTB/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CKS1B/CKS1B/CD24/CD24/CD24/LDHB/LDHB/TUBA1A/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/PSMB6/PSMB6/PSMB6/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/PPP1R14B/ALDH3A1/ALDH3A1/ALDH3A1/IGFBP3/IGFBP3/MYBL2/PHGDH/UBC/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A |
| GO:0009987 | GO:0009987 | cellular process | 120 | 120/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/GSTM3/GSTM3/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/ALPP/ALPP/MT2A/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/UBE2S/HSPB1/HSPB1/MYL6/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CKS1B/CKS1B/CD24/CD24/CD24/LDHB/LDHB/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/CENPN/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/PPP1R14B/TFPI2/ALDH3A1/ALDH3A1/ALDH3A1/CALD1/CALD1/TUBA4A/IGFBP3/IGFBP3/TUBB4B/MYBL2/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4 |
| GO:0016032 | GO:0016032 | viral process | 10 | 10/59 | CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/LAMP1/LAMP1/TOP2A |
| GO:0022414 | GO:0022414 | reproductive process | 29 | 29/59 | KRT8/KRT8/CD9/CD9/CD9/MDK/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CDK1/CDK1/CDK1/CDK1/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4 |
| GO:0032501 | GO:0032501 | multicellular organismal process | 78 | 78/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/GSTM3/GSTM3/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/MYL6/TCOF1/TCOF1/TCOF1/CSTB/CSTB/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/LAMP1/LAMP1/TFPI2/CALD1/CALD1/IGFBP3/IGFBP3/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80 |
| GO:0032502 | GO:0032502 | developmental process | 73 | 73/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/GSTM3/GSTM3/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/MYL6/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/CALD1/CALD1/IGFBP3/IGFBP3/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/CENPF/ANXA2/ANXA2/TOP2A |
| GO:0040007 | GO:0040007 | growth | 23 | 23/59 | FN1/FN1/FN1/FN1/FN1/FN1/CD9/CD9/CD9/MDK/MT2A/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/IGFBP3/IGFBP3/CTNNB1/CTNNB1/CTNNB1 |
| GO:0040011 | GO:0040011 | locomotion | 23 | 23/59 | FN1/FN1/FN1/FN1/FN1/FN1/CD9/CD9/CD9/MDK/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/CD81/CD81/CD81/IGFBP3/IGFBP3/CALR/CALR |
| GO:0042592 | GO:0042592 | homeostatic process | 23 | 23/59 | PRDX1/NQO1/NQO1/MT2A/UBB/UBB/UBE2S/HSPB1/HSPB1/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/CTNNB1/CTNNB1/CTNNB1/CALR/CALR |
| GO:0043473 | GO:0043473 | pigmentation | 0 | 0/59 | |
| GO:0044419 | GO:0044419 | biological process involved in interspecies interaction between organisms | 33 | 33/59 | FN1/FN1/FN1/FN1/FN1/FN1/KRT8/KRT8/PRDX1/NQO1/NQO1/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/CD24/CD24/CD24/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/LAMP1/LAMP1/PPP1R14B/TUBB4B/CALR/CALR |
| GO:0044848 | GO:0044848 | biological phase | 3 | 3/59 | CTNNB1/CTNNB1/CTNNB1 |
| GO:0048511 | GO:0048511 | rhythmic process | 7 | 7/59 | MDK/CDK1/CDK1/CDK1/CDK1/PCNA/TOP2A |
| GO:0048518 | GO:0048518 | positive regulation of biological process | 74 | 74/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/MDK/NQO1/NQO1/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/UBE2S/HSPB1/HSPB1/TFDP1/TFDP1/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/HMGA1/HMGA1/CD81/CD81/CD81/CDK1/CDK1/CDK1/CDK1/CCNB1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/IGFBP3/IGFBP3/MYBL2/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/ANXA2/ANXA2/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4 |
| GO:0048519 | GO:0048519 | negative regulation of biological process | 66 | 66/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/CD9/CD9/CD9/MDK/PGK1/PGK1/NQO1/NQO1/MT2A/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/HSPB1/HSPB1/TFDP1/TFDP1/CSTB/CSTB/JUND/SQSTM1/CD24/CD24/CD24/HMGA1/HMGA1/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/IGFBP3/IGFBP3/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80 |
| GO:0050789 | GO:0050789 | regulation of biological process | 106 | 106/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/MT2A/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/UBE2S/HSPB1/HSPB1/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/CSTB/CSTB/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CKS1B/CKS1B/CD24/CD24/CD24/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/PPP1R14B/IGFBP3/IGFBP3/MYBL2/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4 |
| GO:0050896 | GO:0050896 | response to stimulus | 94 | 94/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/GSTM3/GSTM3/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/MT2A/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/HSPB1/HSPB1/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/PPP1R14B/TFPI2/ALDH3A1/ALDH3A1/ALDH3A1/IGFBP3/IGFBP3/TUBB4B/MYBL2/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80 |
| GO:0051179 | GO:0051179 | localization | 49 | 49/59 | CCND1/CCND1/CD9/CD9/CD9/UBB/UBB/CTSA/CTSA/CTSA/CTSA/HSPB1/HSPB1/TGM2/TGM2/TGM2/TGM2/SQSTM1/CD24/CD24/CD24/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/LAMP1/LAMP1/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/CENPF/ANXA2/ANXA2/NDC80/NDC80 |
| GO:0051703 | GO:0051703 | biological process involved in intraspecies interaction between organisms | 0 | 0/59 | |
| GO:0065007 | GO:0065007 | biological regulation | 107 | 107/59 | FN1/FN1/FN1/FN1/FN1/FN1/CCND1/CCND1/KRT8/KRT8/CD9/CD9/CD9/MDK/PRDX1/PGK1/PGK1/NQO1/NQO1/MT2A/PKM/PKM/PKM/PKM/UBB/UBB/FOSL1/FOSL1/FOSL1/FOSL1/CTSA/CTSA/CTSA/CTSA/UBE2S/HSPB1/HSPB1/TCOF1/TCOF1/TCOF1/TFDP1/TFDP1/CSTB/CSTB/JUND/TGM2/TGM2/TGM2/TGM2/SQSTM1/CKS1B/CKS1B/CD24/CD24/CD24/TUBA1A/RANBP1/RANBP1/RANBP1/RANBP1/RANBP1/HMGA1/HMGA1/CD81/CD81/CD81/PPIF/PPIF/CDK1/CDK1/CDK1/CDK1/CCNB1/SERPINH1/SERPINH1/SERPINH1/FOXM1/FOXM1/FOXM1/FOXM1/LAMP1/LAMP1/PPP1R14B/TFPI2/IGFBP3/IGFBP3/MYBL2/PHGDH/PCNA/CTNNB1/CTNNB1/CTNNB1/CALR/CALR/MCM3/MCM3/MCM3/CENPF/ANXA2/ANXA2/TOP2A/NDC80/NDC80/SMC4/SMC4/SMC4/SMC4 |
| GO:0098754 | GO:0098754 | detoxification | 6 | 6/59 | GSTM3/GSTM3/PRDX1/NQO1/NQO1/MT2A |
In [18]:
drivers_ann <- merge(x=gene,y=drivers,by.x = 'SYMBOL',by.y = 'V1',all = F)
In [28]:
weight <- drivers_ann$weight_shap_total_mean
names(weight) <- drivers_ann$ENTREZID
weight <- weight[order(weight,decreasing = T)]
In [31]:
gsego <- gseGO(gene = weight,
OrgDb = org.Hs.eg.db,
keyType = 'ENTREZID',
ont = "BP",
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
preparing geneSet collections... GSEA analysis... Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : “There are ties in the preranked stats (53.91% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results.” Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : “There are duplicate gene names, fgsea may produce unexpected results.” Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : “All values in the stats vector are greater than zero and scoreType is "std", maybe you should switch to scoreType = "pos".” Warning message in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : “There were 1 pathways for which P-values were not calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)” leading edge analysis... done...
In [32]:
gsego@result
| ID | Description | setSize | enrichmentScore | NES | pvalue | p.adjust | qvalue | rank | leading_edge | core_enrichment | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <int> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| GO:0000819 | GO:0000819 | sister chromatid segregation | 7 | -0.7685950 | -1.907089 | 0.000695424 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=81% | 983/983/983/983/7153/4605/1063/10403/10403/10051/10051/10051/10051 |
| GO:0033044 | GO:0033044 | regulation of chromosome organization | 7 | -0.7685950 | -1.907089 | 0.000695424 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=82% | 983/983/983/983/7153/1063/10403/10403/10051/10051/10051/10051/1499/1499/1499 |
| GO:0098813 | GO:0098813 | nuclear chromosome segregation | 7 | -0.7685950 | -1.907089 | 0.000695424 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=81% | 983/983/983/983/7153/4605/1063/10403/10403/10051/10051/10051/10051 |
| GO:0000070 | GO:0000070 | mitotic sister chromatid segregation | 6 | -0.7622951 | -1.858672 | 0.002765156 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=80% | 983/983/983/983/4605/1063/10403/10403/10051/10051/10051/10051 |
| GO:0051276 | GO:0051276 | chromosome organization | 12 | -0.5603448 | -1.786687 | 0.004917357 | 0.3074144 | 0.3074144 | 64 | tags=96%, list=50%, signal=59% | 3159/3159/891/983/983/983/983/5111/7153/4605/1063/10403/10403/10051/10051/10051/10051/4172/4172/4172/1499/1499/1499 |
| GO:0051983 | GO:0051983 | regulation of chromosome segregation | 5 | -0.7560976 | -1.749891 | 0.007103453 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=79% | 983/983/983/983/1063/10403/10403/10051/10051/10051/10051 |
| GO:0051383 | GO:0051383 | kinetochore organization | 3 | -0.8960000 | -1.685244 | 0.006260319 | 0.3074144 | 0.3074144 | 17 | tags=100%, list=13%, signal=92% | 10403/10403/10051/10051/10051/10051 |
| GO:0006281 | GO:0006281 | DNA repair | 4 | -0.7580645 | -1.631976 | 0.036172583 | 0.3074144 | 0.3074144 | 35 | tags=100%, list=27%, signal=80% | 983/983/983/2305/2305/2305/2305/5111/4172/4172/4172 |
| GO:0010638 | GO:0010638 | positive regulation of organelle organization | 4 | -0.7580645 | -1.631976 | 0.036172583 | 0.3074144 | 0.3074144 | 35 | tags=100%, list=27%, signal=81% | 983/983/983/10051/10051/10051/10051/302/302/1499/1499/1499 |
| GO:0051054 | GO:0051054 | positive regulation of DNA metabolic process | 4 | -0.7580645 | -1.631976 | 0.036172583 | 0.3074144 | 0.3074144 | 35 | tags=100%, list=27%, signal=80% | 983/983/983/2305/2305/2305/2305/5111/1499/1499/1499 |
| GO:0033045 | GO:0033045 | regulation of sister chromatid segregation | 4 | -0.7500000 | -1.614615 | 0.041190693 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=77% | 983/983/983/983/1063/10403/10403 |
| GO:0033047 | GO:0033047 | regulation of mitotic sister chromatid segregation | 4 | -0.7500000 | -1.614615 | 0.041190693 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=77% | 983/983/983/983/1063/10403/10403 |
| GO:0051304 | GO:0051304 | chromosome separation | 4 | -0.7500000 | -1.614615 | 0.041190693 | 0.3074144 | 0.3074144 | 36 | tags=88%, list=28%, signal=67% | 1063/10403/10403/10051/10051/10051/10051 |
| GO:0051783 | GO:0051783 | regulation of nuclear division | 4 | -0.7500000 | -1.614615 | 0.041190693 | 0.3074144 | 0.3074144 | 36 | tags=83%, list=28%, signal=63% | 811/811/1063/10403/10403 |
| GO:1902850 | GO:1902850 | microtubule cytoskeleton organization involved in mitosis | 4 | -0.7500000 | -1.614615 | 0.041190693 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=77% | 983/983/983/983/4605/10403/10403 |
| GO:1905818 | GO:1905818 | regulation of chromosome separation | 4 | -0.7500000 | -1.614615 | 0.041190693 | 0.3074144 | 0.3074144 | 36 | tags=88%, list=28%, signal=67% | 1063/10403/10403/10051/10051/10051/10051 |
| GO:2001251 | GO:2001251 | negative regulation of chromosome organization | 4 | -0.7500000 | -1.614615 | 0.041190693 | 0.3074144 | 0.3074144 | 36 | tags=100%, list=28%, signal=75% | 7153/1063/10403/10403 |
| GO:0060537 | GO:0060537 | muscle tissue development | 5 | 0.8577866 | 1.592443 | 0.005939521 | 0.3074144 | 0.3074144 | 1 | tags=9%, list=1%, signal=10% | 4637 |
| GO:0007517 | GO:0007517 | muscle organ development | 3 | 0.9576972 | 1.590136 | 0.001401771 | 0.3074144 | 0.3074144 | 1 | tags=20%, list=1%, signal=21% | 4637 |
| GO:0033002 | GO:0033002 | muscle cell proliferation | 4 | -0.7338710 | -1.579892 | 0.055052347 | 0.3074144 | 0.3074144 | 38 | tags=100%, list=30%, signal=77% | 3486/983/983/983/983/10403/10403/1499/1499/1499 |
| GO:0051129 | GO:0051129 | negative regulation of cellular component organization | 6 | -0.6475410 | -1.578872 | 0.019173190 | 0.3074144 | 0.3074144 | 50 | tags=100%, list=39%, signal=66% | 10105/891/7153/1063/10403/10403/302/302 |
| GO:0050767 | GO:0050767 | regulation of neurogenesis | 5 | 0.8441406 | 1.567110 | 0.007819628 | 0.3074144 | 0.3074144 | 20 | tags=69%, list=16%, signal=66% | 2335/2335/2335/2335/2335/2335/7052/7052/7052/7052/4192 |
| GO:0051960 | GO:0051960 | regulation of nervous system development | 5 | 0.8441406 | 1.567110 | 0.007819628 | 0.3074144 | 0.3074144 | 20 | tags=69%, list=16%, signal=66% | 2335/2335/2335/2335/2335/2335/7052/7052/7052/7052/4192 |
| GO:0060284 | GO:0060284 | regulation of cell development | 5 | 0.8441406 | 1.567110 | 0.007819628 | 0.3074144 | 0.3074144 | 20 | tags=69%, list=16%, signal=66% | 2335/2335/2335/2335/2335/2335/7052/7052/7052/7052/4192 |
| GO:0097237 | GO:0097237 | cellular response to toxic substance | 3 | 0.9280000 | 1.540828 | 0.007792079 | 0.3074144 | 0.3074144 | 12 | tags=100%, list=9%, signal=94% | 1728/1728/2947/2947/5052 |
| GO:1990748 | GO:1990748 | cellular detoxification | 3 | 0.9280000 | 1.540828 | 0.007792079 | 0.3074144 | 0.3074144 | 12 | tags=100%, list=9%, signal=94% | 1728/1728/2947/2947/5052 |
| GO:0006816 | GO:0006816 | calcium ion transport | 2 | -0.9523810 | -1.538281 | 0.011460554 | 0.3074144 | 0.3074144 | 9 | tags=100%, list=7%, signal=97% | 302/1499/1499/1499 |
| GO:0030001 | GO:0030001 | metal ion transport | 2 | -0.9523810 | -1.538281 | 0.011460554 | 0.3074144 | 0.3074144 | 9 | tags=100%, list=7%, signal=97% | 302/1499/1499/1499 |
| GO:0007507 | GO:0007507 | heart development | 5 | 0.8231498 | 1.528141 | 0.014170618 | 0.3074144 | 0.3074144 | 7 | tags=38%, list=5%, signal=41% | 2335/2335/2335/2335/2335/2335 |
| GO:0010720 | GO:0010720 | positive regulation of cell development | 4 | 0.8552063 | 1.502433 | 0.016048778 | 0.3074144 | 0.3074144 | 20 | tags=79%, list=16%, signal=74% | 2335/2335/2335/2335/2335/2335/7052/7052/7052/7052/4192 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| GO:0051052 | GO:0051052 | regulation of DNA metabolic process | 6 | 0.3126659 | 0.5994505 | 0.9595051 | 0.9907572 | 0.9907572 | 94 | tags=88%, list=73%, signal=27% | 983/983/983/983/2305/2305/2305/2305/5111/4172/4172/4172/1499/1499/1499 |
| GO:0097435 | GO:0097435 | supramolecular fiber organization | 5 | 0.3188595 | 0.5919485 | 0.9690011 | 0.9907572 | 0.9907572 | 29 | tags=20%, list=23%, signal=17% | 3856/3856 |
| GO:0031323 | GO:0031323 | regulation of cellular metabolic process | 30 | 0.2383703 | 0.5824553 | 0.9790210 | 0.9907572 | 0.9907572 | 65 | tags=51%, list=51%, signal=54% | 2335/2335/2335/2335/2335/2335/4192/8061/8061/8061/8061/5476/5476/5476/5476/7027/7027/8878/100133941/100133941/100133941/975/975/975/1163/1163/5315/5315/5315/5315/595/595/6949/6949/6949 |
| GO:0009968 | GO:0009968 | negative regulation of signal transduction | 7 | 0.2924752 | 0.5824015 | 0.9695652 | 0.9907572 | 0.9907572 | 61 | tags=94%, list=48%, signal=56% | 3315/3315/10105/10105/3486/3486/2305/2305/2305/2305/811/811/1499/1499/1499 |
| GO:0010648 | GO:0010648 | negative regulation of cell communication | 7 | 0.2924752 | 0.5824015 | 0.9695652 | 0.9907572 | 0.9907572 | 61 | tags=94%, list=48%, signal=56% | 3315/3315/10105/10105/3486/3486/2305/2305/2305/2305/811/811/1499/1499/1499 |
| GO:0023057 | GO:0023057 | negative regulation of signaling | 7 | 0.2924752 | 0.5824015 | 0.9695652 | 0.9907572 | 0.9907572 | 61 | tags=94%, list=48%, signal=56% | 3315/3315/10105/10105/3486/3486/2305/2305/2305/2305/811/811/1499/1499/1499 |
| GO:0051172 | GO:0051172 | negative regulation of nitrogen compound metabolic process | 16 | 0.2548207 | 0.5814103 | 0.9696049 | 0.9907572 | 0.9907572 | 43 | tags=26%, list=34%, signal=24% | 1728/1728/5476/5476/5476/5476/1476/1476/8878 |
| GO:0051726 | GO:0051726 | regulation of cell cycle | 14 | 0.2562795 | 0.5745464 | 0.9675127 | 0.9907572 | 0.9907572 | 62 | tags=41%, list=48%, signal=29% | 8061/8061/8061/8061/7027/7027/5902/5902/5902/5902/5902/1163/1163/595/595 |
| GO:0031324 | GO:0031324 | negative regulation of cellular metabolic process | 17 | 0.2572748 | 0.5912486 | 0.9827060 | 0.9910678 | 0.9910678 | 7 | tags=14%, list=5%, signal=20% | 2335/2335/2335/2335/2335/2335 |
| GO:0006360 | GO:0006360 | transcription by RNA polymerase I | 1 | 0.5118110 | 0.6827330 | 0.9860558 | 0.9924354 | 0.9924354 | 65 | tags=100%, list=51%, signal=50% | 6949/6949/6949 |
| GO:0009303 | GO:0009303 | rRNA transcription | 1 | 0.5118110 | 0.6827330 | 0.9860558 | 0.9924354 | 0.9924354 | 65 | tags=100%, list=51%, signal=50% | 6949/6949/6949 |
| GO:0014029 | GO:0014029 | neural crest formation | 1 | 0.5118110 | 0.6827330 | 0.9860558 | 0.9924354 | 0.9924354 | 65 | tags=100%, list=51%, signal=50% | 6949/6949/6949 |
| GO:0016072 | GO:0016072 | rRNA metabolic process | 1 | 0.5118110 | 0.6827330 | 0.9860558 | 0.9924354 | 0.9924354 | 65 | tags=100%, list=51%, signal=50% | 6949/6949/6949 |
| GO:0042790 | GO:0042790 | nucleolar large rRNA transcription by RNA polymerase I | 1 | 0.5118110 | 0.6827330 | 0.9860558 | 0.9924354 | 0.9924354 | 65 | tags=100%, list=51%, signal=50% | 6949/6949/6949 |
| GO:0007005 | GO:0007005 | mitochondrion organization | 2 | 0.3809524 | 0.5766925 | 0.9881657 | 0.9936230 | 0.9936230 | 81 | tags=100%, list=63%, signal=38% | 8878/10105/10105 |
| GO:0010821 | GO:0010821 | regulation of mitochondrion organization | 2 | 0.3809524 | 0.5766925 | 0.9881657 | 0.9936230 | 0.9936230 | 81 | tags=100%, list=63%, signal=38% | 8878/10105/10105 |
| GO:0006357 | GO:0006357 | regulation of transcription by RNA polymerase II | 15 | 0.2273327 | 0.5134139 | 0.9888325 | 0.9936230 | 0.9936230 | 62 | tags=36%, list=48%, signal=25% | 8061/8061/8061/8061/7027/7027/8878/975/975/975/595/595 |
| GO:0006366 | GO:0006366 | transcription by RNA polymerase II | 15 | 0.2273327 | 0.5134139 | 0.9888325 | 0.9936230 | 0.9936230 | 62 | tags=36%, list=48%, signal=25% | 8061/8061/8061/8061/7027/7027/8878/975/975/975/595/595 |
| GO:0031055 | GO:0031055 | chromatin remodeling at centromere | 1 | -0.5118110 | -0.6713375 | 0.9900000 | 0.9938969 | 0.9938969 | 64 | tags=100%, list=50%, signal=50% | |
| GO:0034080 | GO:0034080 | CENP-A containing chromatin assembly | 1 | -0.5118110 | -0.6713375 | 0.9900000 | 0.9938969 | 0.9938969 | 64 | tags=100%, list=50%, signal=50% | |
| GO:0044092 | GO:0044092 | negative regulation of molecular function | 4 | 0.3145161 | 0.5525443 | 0.9903030 | 0.9938969 | 0.9938969 | 91 | tags=100%, list=71%, signal=31% | 1476/1476/3315/3315/10105/10105/871/871/871 |
| GO:0043086 | GO:0043086 | negative regulation of catalytic activity | 3 | 0.3120000 | 0.5180370 | 0.9960988 | 0.9973977 | 0.9973977 | 91 | tags=100%, list=71%, signal=31% | 1476/1476/3315/3315/871/871/871 |
| GO:0045931 | GO:0045931 | positive regulation of mitotic cell cycle | 4 | 0.2873663 | 0.5048472 | 0.9963636 | 0.9973977 | 0.9973977 | 94 | tags=100%, list=73%, signal=29% | 7027/595/595/891/983/983/983/983 |
| GO:1901989 | GO:1901989 | positive regulation of cell cycle phase transition | 4 | 0.2873663 | 0.5048472 | 0.9963636 | 0.9973977 | 0.9973977 | 94 | tags=100%, list=73%, signal=29% | 7027/595/595/891/983/983/983/983 |
| GO:1901992 | GO:1901992 | positive regulation of mitotic cell cycle phase transition | 4 | 0.2873663 | 0.5048472 | 0.9963636 | 0.9973977 | 0.9973977 | 94 | tags=100%, list=73%, signal=29% | 7027/595/595/891/983/983/983/983 |
| GO:0000280 | GO:0000280 | nuclear division | 11 | 0.2212110 | 0.4790455 | 0.9948347 | 0.9973977 | 0.9973977 | 68 | tags=33%, list=53%, signal=19% | 27338/5902/5902/5902/5902/5902/7314/7314 |
| GO:0048285 | GO:0048285 | organelle fission | 11 | 0.2212110 | 0.4790455 | 0.9948347 | 0.9973977 | 0.9973977 | 68 | tags=33%, list=53%, signal=19% | 27338/5902/5902/5902/5902/5902/7314/7314 |
| GO:0007049 | GO:0007049 | cell cycle | 25 | 0.2000446 | 0.4778260 | 0.9969970 | 0.9973977 | 0.9973977 | 74 | tags=41%, list=58%, signal=29% | 27338/8061/8061/8061/8061/7027/7027/5902/5902/5902/5902/5902/1163/1163/595/595/55839/7314/7314/3727/7846 |
| GO:0000278 | GO:0000278 | mitotic cell cycle | 18 | 0.1943552 | 0.4484935 | 0.9969666 | 0.9973977 | 0.9973977 | 62 | tags=31%, list=48%, signal=23% | 27338/7027/7027/5902/5902/5902/5902/5902/1163/1163/595/595 |
| GO:0022402 | GO:0022402 | cell cycle process | 21 | 0.1944509 | 0.4559383 | 0.9989940 | 0.9989940 | 0.9989940 | 68 | tags=34%, list=53%, signal=24% | 27338/7027/7027/5902/5902/5902/5902/5902/1163/1163/595/595/55839/7314/7314 |
In [41]:
drivers[drivers$is_tf]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| FOSL1 | 0.0042657685 | 0.0005058976 | -9.429040e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| TFDP1 | 0.0033304277 | 0.0003013794 | -9.927206e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| JUND | 0.0016843241 | 0.0003056773 | 1.057126e-04 | 10 | TRUE | FALSE | FALSE | FALSE |
| HMGA1 | 0.0012568032 | 0.0005449500 | -4.842230e-05 | 7 | TRUE | FALSE | FALSE | FALSE |
| FOXM1 | 0.0006686676 | 0.0001551108 | -7.297643e-06 | 5 | TRUE | FALSE | FALSE | FALSE |
| MYBL2 | 0.0002402041 | 0.0001968738 | 6.863686e-06 | 2 | TRUE | FALSE | FALSE | FALSE |
In [42]:
drivers[drivers$is_in_FAM]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE |
| ALDH3A1 | 0.001305538 | 0.0001966169 | 4.354592e-07 | 4 | FALSE | TRUE | FALSE | TRUE |
In [43]:
drivers[drivers$is_in_ROS]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| PRDX1 | 0.005753293 | 0.0005948305 | 0.0001307083 | 10 | FALSE | FALSE | TRUE | TRUE |
| NQO1 | 0.007189172 | 0.0005834166 | 0.0001745988 | 10 | FALSE | FALSE | TRUE | TRUE |
In [45]:
drivers[drivers$is_in_Pathway]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE |
| PRDX1 | 0.005753293 | 0.0005948305 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE |
| NQO1 | 0.007189172 | 0.0005834166 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE |
| ALDH3A1 | 0.001305538 | 0.0001966169 | 4.354592e-07 | 4 | FALSE | TRUE | FALSE | TRUE |
In [61]:
drivers$rank = 1:dim(drivers)[1]
In [62]:
drivers[drivers$is_in_Pathway]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank |
|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <int> |
| H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE | 7 |
| PRDX1 | 0.005753293 | 0.0005948305 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 10 |
| NQO1 | 0.007189172 | 0.0005834166 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 12 |
| ALDH3A1 | 0.001305538 | 0.0001966169 | 4.354592e-07 | 4 | FALSE | TRUE | FALSE | TRUE | 46 |
In [48]:
driver_info <- fread(file.path(read_dir,'driver_info_9.csv'))
In [49]:
driver_info[driver_info$is_in_Pathway]
| V1 | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | weight_shap_total | weight_shap_total_dir | weight_shap_0 | weight_shap_0_dir | weight_shap_1 | ⋯ | weight_grad_0 | weight_grad_0_dir | weight_grad_1 | weight_grad_1_dir | is_driver_shap_total | is_driver_shap_0 | is_driver_shap_1 | is_driver_grad_total | is_driver_grad_0 | is_driver_grad_1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> |
| H2AFZ | FALSE | TRUE | FALSE | TRUE | 0.001396736 | -4.011072e-05 | 0.001240849 | 0.001474716 | 0.001561170 | ⋯ | 0.001331874 | 6.733973e-11 | 0.0008687093 | 4.257443e-09 | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE |
| PRDX1 | FALSE | FALSE | TRUE | TRUE | 0.005420250 | -4.785569e-04 | 0.005700868 | -0.009741519 | 0.005124247 | ⋯ | 0.002573374 | 1.301101e-10 | 0.0026004973 | 1.291586e-08 | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE |
| NQO1 | FALSE | FALSE | TRUE | TRUE | 0.007288831 | -1.938082e-04 | 0.007157244 | -0.012187278 | 0.007427631 | ⋯ | 0.003293566 | 1.665231e-10 | 0.0031686146 | 1.573753e-08 | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE |
In [209]:
kk <- enrichKEGG(gene = gene$ENTREZID,
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.1)
head(kk)
| category | subcategory | ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| hsa04218 | Cellular Processes | Cell growth and death | hsa04218 | Cellular senescence | 7/49 | 156/8753 | 2.287756e-05 | 0.001716776 | 0.001392998 | 595/8878/983/891/2305/3486/4605 | 7 |
| hsa04110 | Cellular Processes | Cell growth and death | hsa04110 | Cell cycle | 7/49 | 157/8753 | 2.384412e-05 | 0.001716776 | 0.001392998 | 595/7027/983/891/5111/4172/10403 | 7 |
| hsa00010 | Metabolism | Carbohydrate metabolism | hsa00010 | Glycolysis / Gluconeogenesis | 4/49 | 67/8753 | 5.127849e-04 | 0.022663076 | 0.018388900 | 5230/5315/3945/218 | 4 |
| hsa05012 | Human Diseases | Neurodegenerative disease | hsa05012 | Parkinson disease | 7/49 | 266/8753 | 6.370286e-04 | 0.022663076 | 0.018388900 | 7314/7846/10105/5694/7277/10383/7316 | 7 |
| hsa04115 | Cellular Processes | Cell growth and death | hsa04115 | p53 signaling pathway | 4/49 | 75/8753 | 7.869124e-04 | 0.022663076 | 0.018388900 | 595/983/891/3486 | 4 |
| hsa05022 | Human Diseases | Neurodegenerative disease | hsa05022 | Pathways of neurodegeneration - multiple diseases | 9/49 | 476/8753 | 1.128896e-03 | 0.027093516 | 0.021983774 | 7314/8878/7846/10105/5694/7277/10383/7316/1499 | 9 |
In [208]:
?enrichKEGG
In [66]:
plot(table(kk@result$subcategory))
In [53]:
browseKEGG(kk, 'hsa05208')
Error in browseURL(url): 'browser' must be a non-empty character string
Traceback:
1. browseKEGG(kk, "hsa05208")
2. browseURL(url)
3. stop("'browser' must be a non-empty character string")
In [54]:
library("pathview")
##############################################################################
Pathview is an open source software package distributed under GNU General
Public License version 3 (GPLv3). Details of GPLv3 is available at
http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
formally cite the original Pathview paper (not just mention it) in publications
or products. For details, do citation("pathview") within R.
The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
license agreement (details at http://www.kegg.jp/kegg/legal.html).
##############################################################################
In [56]:
hsa05208 <- pathview(gene.data = weight,
pathway.id = "hsa05208",
species = "hsa",
limit = list(gene=max(abs(weight)), cpd=1))
Info: Downloading xml files for hsa05208, 1/1 pathways.. Info: Downloading png files for hsa05208, 1/1 pathways.. 'select()' returned 1:1 mapping between keys and columns Info: Working in directory /sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master Info: Writing image file hsa05208.pathview.png

In [183]:
kk@result['hsa05208',]
| category | subcategory | ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| hsa05208 | Human Diseases | Cancer: overview | hsa05208 | Chemical carcinogenesis - reactive oxygen species | 4/72 | 331/11008 | 0.1707392 | 0.2815419 | 0.1926339 | P21266/Q6FGJ9/P15559/P30405 | 4 |
In [ ]:
bngeneplot(results = kk@result, exp = vsted, pathNum = 17)
In [190]:
set.seed(123)
counts <- matrix(sample(1:100, 100, replace=TRUE), nrow=10, ncol=10)
numbers <- 1:10
# 将数值向量转换为字符向量
char_vector <- as.character(numbers)
In [193]:
library(DESeq2)
#counts = read.table("GSE133624_reads-count-all-sample.txt", header=1, row.names=1)
meta = sapply(char_vector, function (x) substring(x,1,1))
meta = data.frame(meta)
colnames(meta) = c("Condition")
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = meta,
design= ~ Condition)
v = varianceStabilizingTransformation(dds, blind=FALSE)
vsted = assay(v)
Warning message in DESeqDataSet(se, design = design, ignoreRank): “some variables in design formula are characters, converting to factors” -- note: fitType='parametric', but the dispersion trend was not well captured by the function: y = a/x + b, and a local regression fit was automatically substituted. specify fitType='local' or 'mean' to avoid this message next time. Warning message in lfproc(x, y, weights = weights, cens = cens, base = base, geth = geth, : “Estimated rdf < 1.0; not estimating variance”
In [194]:
vsted
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|
| 4.724133 | 5.491294 | 4.535267 | 4.495636 | 4.495066 | 5.532322 | 4.567998 | 5.963493 | 5.827968 | 4.496718 |
| 5.960347 | 5.513097 | 4.495066 | 5.232386 | 5.040282 | 5.135537 | 4.718235 | 5.319920 | 4.515816 | 4.496024 |
| 5.690746 | 4.928036 | 5.272539 | 4.495066 | 4.495538 | 4.495066 | 5.866435 | 4.855198 | 5.963629 | 5.291622 |
| 4.495067 | 5.513097 | 4.495066 | 4.495066 | 5.931807 | 5.657654 | 5.191741 | 5.963048 | 4.941186 | 5.918016 |
| 5.935060 | 4.621886 | 5.961474 | 5.706913 | 4.551802 | 5.865337 | 5.904000 | 5.768884 | 4.577091 | 4.495066 |
| 5.317416 | 5.531489 | 4.957732 | 5.963507 | 5.895158 | 5.802080 | 4.495066 | 5.042022 | 5.963575 | 5.880892 |
| 5.660109 | 4.495066 | 5.958095 | 4.631964 | 4.511030 | 4.495074 | 5.897695 | 4.495066 | 5.126250 | 5.901836 |
| 5.367441 | 5.549653 | 5.960465 | 4.885332 | 5.919073 | 4.508134 | 4.495066 | 4.495068 | 5.403415 | 5.522206 |
| 4.495067 | 5.652659 | 5.319215 | 5.957193 | 5.583002 | 4.495067 | 5.569531 | 5.955827 | 4.815738 | 5.390085 |
| 4.528517 | 5.015393 | 5.955680 | 5.933313 | 5.652573 | 5.597127 | 5.823828 | 4.568487 | 4.495067 | 4.495071 |
In [222]:
mtx <- drivers[,c('weight_shap_total_mean')]
#rownames(mtx) <- drivers$V1
mtx <- as.matrix(mtx)
replicated_matrix <- matrix(mtx, nrow=nrow(mtx), ncol=10, byrow=FALSE)
rownames(replicated_matrix) <- drivers$V1
colnames(replicated_matrix) <- seq(1,10)
In [227]:
kk
# # over-representation test # #...@organism hsa #...@ontology KEGG #...@keytype kegg #...@gene chr [1:59] "2335" "595" "3856" "2947" "928" "9741" "4192" "5052" "5230" ... #...pvalues adjusted by 'BH' with cutoff <0.1 #...11 enriched terms found 'data.frame': 11 obs. of 11 variables: $ category : chr "Cellular Processes" "Cellular Processes" "Metabolism" "Human Diseases" ... $ subcategory: chr "Cell growth and death" "Cell growth and death" "Carbohydrate metabolism" "Neurodegenerative disease" ... $ ID : chr "hsa04218" "hsa04110" "hsa00010" "hsa05012" ... $ Description: chr "Cellular senescence" "Cell cycle" "Glycolysis / Gluconeogenesis" "Parkinson disease" ... $ GeneRatio : chr "7/49" "7/49" "4/49" "7/49" ... $ BgRatio : chr "156/8753" "157/8753" "67/8753" "266/8753" ... $ pvalue : num 2.29e-05 2.38e-05 5.13e-04 6.37e-04 7.87e-04 ... $ p.adjust : num 0.00172 0.00172 0.02266 0.02266 0.02266 ... $ qvalue : num 0.00139 0.00139 0.01839 0.01839 0.01839 ... $ geneID : chr "595/8878/983/891/2305/3486/4605" "595/7027/983/891/5111/4172/10403" "5230/5315/3945/218" "7314/7846/10105/5694/7277/10383/7316" ... $ Count : int 7 7 4 7 4 9 4 5 5 6 ... #...Citation T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
In [211]:
pway = setReadable(kk, OrgDb=org.Hs.eg.db, keyType="ENTREZID")
In [231]:
ttt <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
rownames(ttt) <- ttt$SYMBOL
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“1.67% of input gene IDs are fail to map...”
In [236]:
rownames(replicated_matrix) <- ttt[rownames(replicated_matrix),'ENTREZID']
In [276]:
noise <- matrix(rnorm(25, mean=1, sd=1),nrow=nrow(replicated_matrix),ncol=ncol(replicated_matrix))
# 将随机噪声添加到原始矩阵
noisy_matrix <- replicated_matrix*1000 + noise
rownames(noisy_matrix) <- drivers$V1
colnames(noisy_matrix) <- seq(1,10)
rownames(noisy_matrix) <- ttt[rownames(noisy_matrix),'ENTREZID']
In [280]:
CBNplot::bngeneplot(results = pway, exp = noisy_matrix, expRow = 'ENTREZID',pathNum = 62)
'select()' returned 1:1 mapping between keys and columns 'select()' returned 1:1 mapping between keys and columns no edge present in graph
'error'
In [264]:
which(kk@result$ID == 'hsa05208')
62
In [243]:
attributes(pway)$result
| category | subcategory | ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| hsa04218 | Cellular Processes | Cell growth and death | hsa04218 | Cellular senescence | 7/49 | 156/8753 | 2.287756e-05 | 0.001716776 | 0.001392998 | CCND1/SQSTM1/CDK1/CCNB1/FOXM1/IGFBP3/MYBL2 | 7 |
| hsa04110 | Cellular Processes | Cell growth and death | hsa04110 | Cell cycle | 7/49 | 157/8753 | 2.384412e-05 | 0.001716776 | 0.001392998 | CCND1/TFDP1/CDK1/CCNB1/PCNA/MCM3/NDC80 | 7 |
| hsa00010 | Metabolism | Carbohydrate metabolism | hsa00010 | Glycolysis / Gluconeogenesis | 4/49 | 67/8753 | 5.127849e-04 | 0.022663076 | 0.018388900 | PGK1/PKM/LDHB/ALDH3A1 | 4 |
| hsa05012 | Human Diseases | Neurodegenerative disease | hsa05012 | Parkinson disease | 7/49 | 266/8753 | 6.370286e-04 | 0.022663076 | 0.018388900 | UBB/TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B/UBC | 7 |
| hsa04115 | Cellular Processes | Cell growth and death | hsa04115 | p53 signaling pathway | 4/49 | 75/8753 | 7.869124e-04 | 0.022663076 | 0.018388900 | CCND1/CDK1/CCNB1/IGFBP3 | 4 |
| hsa05022 | Human Diseases | Neurodegenerative disease | hsa05022 | Pathways of neurodegeneration - multiple diseases | 9/49 | 476/8753 | 1.128896e-03 | 0.027093516 | 0.021983774 | UBB/SQSTM1/TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B/UBC/CTNNB1 | 9 |
| hsa04540 | Cellular Processes | Cellular community - eukaryotes | hsa04540 | Gap junction | 4/49 | 88/8753 | 1.431436e-03 | 0.027422076 | 0.022250369 | TUBA1A/CDK1/TUBA4A/TUBB4B | 4 |
| hsa04145 | Cellular Processes | Transport and catabolism | hsa04145 | Phagosome | 5/49 | 152/8753 | 1.523449e-03 | 0.027422076 | 0.022250369 | TUBA1A/LAMP1/TUBA4A/TUBB4B/CALR | 5 |
| hsa04530 | Cellular Processes | Cellular community - eukaryotes | hsa04530 | Tight junction | 5/49 | 170/8753 | 2.490451e-03 | 0.039847219 | 0.032332174 | CCND1/MYL6/TUBA1A/TUBA4A/PCNA | 5 |
| hsa05016 | Human Diseases | Neurodegenerative disease | hsa05016 | Huntington disease | 6/49 | 306/8753 | 6.856213e-03 | 0.097172299 | 0.078845944 | TGM2/TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B | 6 |
| hsa05418 | Human Diseases | Cardiovascular disease | hsa05418 | Fluid shear stress and atherosclerosis | 4/49 | 139/8753 | 7.422884e-03 | 0.097172299 | 0.078845944 | GSTM3/NQO1/SQSTM1/CTNNB1 | 4 |
| hsa01230 | Metabolism | Global and overview maps | hsa01230 | Biosynthesis of amino acids | 3/49 | 75/8753 | 8.393387e-03 | 0.100720641 | 0.081725082 | PGK1/PKM/PHGDH | 3 |
| hsa05132 | Human Diseases | Infectious disease: bacterial | hsa05132 | Salmonella infection | 5/49 | 247/8753 | 1.191903e-02 | 0.129047398 | 0.104709512 | TUBA1A/TUBA4A/TUBB4B/CTNNB1/ANXA2 | 5 |
| hsa04140 | Cellular Processes | Transport and catabolism | hsa04140 | Autophagy - animal | 4/49 | 165/8753 | 1.335173e-02 | 0.129047398 | 0.104709512 | UBB/SQSTM1/LAMP1/UBC | 4 |
| hsa05225 | Human Diseases | Cancer: specific types | hsa05225 | Hepatocellular carcinoma | 4/49 | 168/8753 | 1.418522e-02 | 0.129047398 | 0.104709512 | CCND1/GSTM3/NQO1/CTNNB1 | 4 |
| hsa05222 | Human Diseases | Cancer: specific types | hsa05222 | Small cell lung cancer | 3/49 | 92/8753 | 1.461035e-02 | 0.129047398 | 0.104709512 | FN1/CCND1/CKS1B | 3 |
| hsa03030 | Genetic Information Processing | Replication and repair | hsa03030 | DNA replication | 2/49 | 36/8753 | 1.713450e-02 | 0.129047398 | 0.104709512 | PCNA/MCM3 | 2 |
| hsa05020 | Human Diseases | Neurodegenerative disease | hsa05020 | Prion disease | 5/49 | 272/8753 | 1.746331e-02 | 0.129047398 | 0.104709512 | TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B | 5 |
| hsa05216 | Human Diseases | Cancer: specific types | hsa05216 | Thyroid cancer | 2/49 | 37/8753 | 1.804957e-02 | 0.129047398 | 0.104709512 | CCND1/CTNNB1 | 2 |
| hsa05146 | Human Diseases | Infectious disease: parasitic | hsa05146 | Amoebiasis | 3/49 | 102/8753 | 1.921568e-02 | 0.129047398 | 0.104709512 | FN1/PRDX1/HSPB1 | 3 |
| hsa05010 | Human Diseases | Neurodegenerative disease | hsa05010 | Alzheimer disease | 6/49 | 384/8753 | 1.947279e-02 | 0.129047398 | 0.104709512 | TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B/CTNNB1 | 6 |
| hsa04137 | Cellular Processes | Transport and catabolism | hsa04137 | Mitophagy - animal | 3/49 | 103/8753 | 1.971557e-02 | 0.129047398 | 0.104709512 | UBB/SQSTM1/UBC | 3 |
| hsa04814 | Cellular Processes | Cell motility | hsa04814 | Motor proteins | 4/49 | 193/8753 | 2.243184e-02 | 0.136869617 | 0.111056488 | MYL6/TUBA1A/TUBA4A/TUBB4B | 4 |
| hsa05167 | Human Diseases | Infectious disease: viral | hsa05167 | Kaposi sarcoma-associated herpesvirus infection | 4/49 | 194/8753 | 2.281160e-02 | 0.136869617 | 0.111056488 | CCND1/UBB/UBC/CTNNB1 | 4 |
| hsa01200 | Metabolism | Global and overview maps | hsa01200 | Carbon metabolism | 3/49 | 115/8753 | 2.627589e-02 | 0.148581769 | 0.120559769 | PGK1/PKM/PHGDH | 3 |
| hsa05203 | Human Diseases | Cancer: overview | hsa05203 | Viral carcinogenesis | 4/49 | 204/8753 | 2.682726e-02 | 0.148581769 | 0.120559769 | CCND1/PKM/RANBP1/CDK1 | 4 |
| hsa00620 | Metabolism | Carbohydrate metabolism | hsa00620 | Pyruvate metabolism | 2/49 | 47/8753 | 2.827941e-02 | 0.150823516 | 0.122378730 | PKM/LDHB | 2 |
| hsa00270 | Metabolism | Amino acid metabolism | hsa00270 | Cysteine and methionine metabolism | 2/49 | 52/8753 | 3.408291e-02 | 0.174152303 | 0.141307790 | LDHB/PHGDH | 2 |
| hsa05166 | Human Diseases | Infectious disease: viral | hsa05166 | Human T-cell leukemia virus 1 infection | 4/49 | 222/8753 | 3.507234e-02 | 0.174152303 | 0.141307790 | CCND1/FOSL1/RANBP1/CALR | 4 |
| hsa04142 | Cellular Processes | Transport and catabolism | hsa04142 | Lysosome | 3/49 | 132/8753 | 3.734000e-02 | 0.179231989 | 0.145429464 | LAPTM4A/CTSA/LAMP1 | 3 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| hsa01522 | Human Diseases | Drug resistance: antineoplastic | hsa01522 | Endocrine resistance | 1/49 | 98/8753 | 0.4249125 | 0.5320644 | 0.4317189 | CCND1 | 1 |
| hsa04916 | Organismal Systems | Endocrine system | hsa04916 | Melanogenesis | 1/49 | 101/8753 | 0.4346260 | 0.5388652 | 0.4372371 | CTNNB1 | 1 |
| hsa05142 | Human Diseases | Infectious disease: parasitic | hsa05142 | Chagas disease | 1/49 | 102/8753 | 0.4378279 | 0.5388652 | 0.4372371 | CALR | 1 |
| hsa04350 | Environmental Information Processing | Signal transduction | hsa04350 | TGF-beta signaling pathway | 1/49 | 108/8753 | 0.4566700 | 0.5572921 | 0.4521888 | TFDP1 | 1 |
| hsa05145 | Human Diseases | Infectious disease: parasitic | hsa05145 | Toxoplasmosis | 1/49 | 111/8753 | 0.4658576 | 0.5637268 | 0.4574099 | PPIF | 1 |
| hsa04928 | Organismal Systems | Endocrine system | hsa04928 | Parathyroid hormone synthesis, secretion and action | 1/49 | 114/8753 | 0.4748929 | 0.5687063 | 0.4614503 | JUND | 1 |
| hsa04670 | Organismal Systems | Immune system | hsa04670 | Leukocyte transendothelial migration | 1/49 | 115/8753 | 0.4778713 | 0.5687063 | 0.4614503 | CTNNB1 | 1 |
| hsa04935 | Organismal Systems | Endocrine system | hsa04935 | Growth hormone synthesis, secretion and action | 1/49 | 120/8753 | 0.4925167 | 0.5799772 | 0.4705955 | IGFBP3 | 1 |
| hsa04152 | Environmental Information Processing | Signal transduction | hsa04152 | AMPK signaling pathway | 1/49 | 121/8753 | 0.4953972 | 0.5799772 | 0.4705955 | CCND1 | 1 |
| hsa04010 | Environmental Information Processing | Signal transduction | hsa04010 | MAPK signaling pathway | 2/49 | 299/8753 | 0.5028329 | 0.5839350 | 0.4738069 | HSPB1/JUND | 2 |
| hsa05135 | Human Diseases | Infectious disease: bacterial | hsa05135 | Yersinia infection | 1/49 | 137/8753 | 0.5393638 | 0.6174862 | 0.5010305 | FN1 | 1 |
| hsa05162 | Human Diseases | Infectious disease: viral | hsa05162 | Measles | 1/49 | 138/8753 | 0.5419835 | 0.6174862 | 0.5010305 | CCND1 | 1 |
| hsa04371 | Environmental Information Processing | Signal transduction | hsa04371 | Apelin signaling pathway | 1/49 | 139/8753 | 0.5445886 | 0.6174862 | 0.5010305 | CCND1 | 1 |
| hsa04550 | Cellular Processes | Cellular community - eukaryotes | hsa04550 | Signaling pathways regulating pluripotency of stem cells | 1/49 | 143/8753 | 0.5548645 | 0.6242226 | 0.5064964 | CTNNB1 | 1 |
| hsa04217 | Cellular Processes | Cell growth and death | hsa04217 | Necroptosis | 1/49 | 159/8753 | 0.5937447 | 0.6575824 | 0.5335647 | SQSTM1 | 1 |
| hsa05161 | Human Diseases | Infectious disease: viral | hsa05161 | Hepatitis B | 1/49 | 162/8753 | 0.6006549 | 0.6575824 | 0.5335647 | PCNA | 1 |
| hsa04151 | Environmental Information Processing | Signal transduction | hsa04151 | PI3K-Akt signaling pathway | 2/49 | 359/8753 | 0.6030588 | 0.6575824 | 0.5335647 | FN1/CCND1 | 2 |
| hsa04022 | Environmental Information Processing | Signal transduction | hsa04022 | cGMP-PKG signaling pathway | 1/49 | 166/8753 | 0.6096897 | 0.6575824 | 0.5335647 | PPIF | 1 |
| hsa04630 | Environmental Information Processing | Signal transduction | hsa04630 | JAK-STAT signaling pathway | 1/49 | 166/8753 | 0.6096897 | 0.6575824 | 0.5335647 | CCND1 | 1 |
| hsa03008 | Genetic Information Processing | Translation | hsa03008 | Ribosome biogenesis in eukaryotes | 1/49 | 167/8753 | 0.6119170 | 0.6575824 | 0.5335647 | TCOF1 | 1 |
| hsa04141 | Genetic Information Processing | Folding, sorting and degradation | hsa04141 | Protein processing in endoplasmic reticulum | 1/49 | 170/8753 | 0.6185242 | 0.6597591 | 0.5353309 | CALR | 1 |
| hsa05152 | Human Diseases | Infectious disease: bacterial | hsa05152 | Tuberculosis | 1/49 | 180/8753 | 0.6397623 | 0.6773954 | 0.5496410 | LAMP1 | 1 |
| hsa04613 | Organismal Systems | Immune system | hsa04613 | Neutrophil extracellular trap formation | 1/49 | 191/8753 | 0.6617873 | 0.6945888 | 0.5635918 | PPIF | 1 |
| hsa05202 | Human Diseases | Cancer: overview | hsa05202 | Transcriptional misregulation in cancer | 1/49 | 193/8753 | 0.6656476 | 0.6945888 | 0.5635918 | IGFBP3 | 1 |
| hsa05415 | Human Diseases | Cardiovascular disease | hsa05415 | Diabetic cardiomyopathy | 1/49 | 203/8753 | 0.6843109 | 0.7089264 | 0.5752254 | PPIF | 1 |
| hsa04015 | Environmental Information Processing | Signal transduction | hsa04015 | Rap1 signaling pathway | 1/49 | 210/8753 | 0.6967640 | 0.7166716 | 0.5815098 | CTNNB1 | 1 |
| hsa04810 | Cellular Processes | Cell motility | hsa04810 | Regulation of actin cytoskeleton | 1/49 | 229/8753 | 0.7281899 | 0.7384461 | 0.5991777 | FN1 | 1 |
| hsa04820 | NA | NA | hsa04820 | Cytoskeleton in muscle cells | 1/49 | 229/8753 | 0.7281899 | 0.7384461 | 0.5991777 | FN1 | 1 |
| hsa04020 | Environmental Information Processing | Signal transduction | hsa04020 | Calcium signaling pathway | 1/49 | 253/8753 | 0.7633565 | 0.7686947 | 0.6237215 | PPIF | 1 |
| hsa05206 | Human Diseases | Cancer: overview | hsa05206 | MicroRNAs in cancer | 1/49 | 310/8753 | 0.8299790 | 0.8299790 | 0.6734478 | CCND1 | 1 |
In [239]:
pway
# # over-representation test # #...@organism hsa #...@ontology KEGG #...@keytype ENTREZID #...@gene chr [1:59] "2335" "595" "3856" "2947" "928" "9741" "4192" "5052" "5230" ... #...pvalues adjusted by 'BH' with cutoff <0.1 #...11 enriched terms found 'data.frame': 11 obs. of 11 variables: $ category : chr "Cellular Processes" "Cellular Processes" "Metabolism" "Human Diseases" ... $ subcategory: chr "Cell growth and death" "Cell growth and death" "Carbohydrate metabolism" "Neurodegenerative disease" ... $ ID : chr "hsa04218" "hsa04110" "hsa00010" "hsa05012" ... $ Description: chr "Cellular senescence" "Cell cycle" "Glycolysis / Gluconeogenesis" "Parkinson disease" ... $ GeneRatio : chr "7/49" "7/49" "4/49" "7/49" ... $ BgRatio : chr "156/8753" "157/8753" "67/8753" "266/8753" ... $ pvalue : num 2.29e-05 2.38e-05 5.13e-04 6.37e-04 7.87e-04 ... $ p.adjust : num 0.00172 0.00172 0.02266 0.02266 0.02266 ... $ qvalue : num 0.00139 0.00139 0.01839 0.01839 0.01839 ... $ geneID : chr "CCND1/SQSTM1/CDK1/CCNB1/FOXM1/IGFBP3/MYBL2" "CCND1/TFDP1/CDK1/CCNB1/PCNA/MCM3/NDC80" "PGK1/PKM/LDHB/ALDH3A1" "UBB/TUBA1A/PPIF/PSMB6/TUBA4A/TUBB4B/UBC" ... $ Count : int 7 7 4 7 4 9 4 5 5 6 ... #...Citation T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
In [76]:
pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
In [77]:
pathway_hallmark
| HALLMARK_TNFA_SIGNALING_VIA_NFKB | HALLMARK_HYPOXIA | HALLMARK_CHOLESTEROL_HOMEOSTASIS | HALLMARK_TGF_BETA_SIGNALING | HALLMARK_DNA_REPAIR | HALLMARK_G2M_CHECKPOINT | HALLMARK_APOPTOSIS | HALLMARK_NOTCH_SIGNALING | HALLMARK_ADIPOGENESIS | HALLMARK_ESTROGEN_RESPONSE_EARLY | ⋯ | HALLMARK_IL2_STAT5_SIGNALING | HALLMARK_ALLOGRAFT_REJECTION | HALLMARK_KRAS_SIGNALING_UP | FATTY_ACID_BETA_OXIDATION | S.GENES.ITAY | G2M.GENES.ITAY | CELL_CYCLE | NRF2_ROMORO | KEGG_GLUTATHIONE_METABOLISM | GO_ANTIOXIDANT_ACTIVITY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | ⋯ | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | |
| 1 | JUNB | PGK1 | FDPS | TGFBR1 | POLR2H | AURKA | CASP3 | JAG1 | FABP4 | GREB1 | ⋯ | SOCS2 | PTPRC | ANGPTL4 | ACADM | MCM5 | HMGB2 | MCM5 | TXNRD1 | ANPEP | ALB |
| 2 | CXCL2 | PDK1 | CYP51A1 | SMAD7 | POLR2A | CCNA2 | CASP9 | NOTCH3 | ADIPOQ | CA12 | ⋯ | CISH | IL12B | ITGA2 | ACADS | PCNA | CDK1 | PCNA | TKT | G6PD | ALOX5AP |
| 3 | ATF3 | GBE1 | IDI1 | TGFB1 | POLR2G | TOP2A | DFFA | NOTCH2 | PPARG | SLC9A3R1 | ⋯ | PIM1 | TGFB1 | SPRY2 | ACADVL | TYMS | NUSAP1 | TYMS | SRXN1 | GCLC | APOA4 |
| 4 | NFKBIA | PFKL | FDFT1 | SMURF2 | POLR2E | CCNB2 | CASP7 | APH1A | LIPE | MYB | ⋯ | IL2RA | IL12A | HBEGF | BDH2 | FEN1 | UBE2C | FEN1 | NQO1 | GCLM | APOE |
| 5 | TNFAIP3 | ALDOA | DHCR7 | SMURF1 | POLR2J | CENPA | CFLAR | HES1 | DGAT1 | ANXA9 | ⋯ | TNFRSF4 | CD3E | RBP4 | CPT1A | MCM2 | BIRC5 | MCM2 | GCLC | GGCT | APOM |
| 6 | PTGS2 | ENO2 | SQLE | BMPR2 | POLR2F | BIRC5 | BIRC3 | CCND1 | LPL | IGFBP4 | ⋯ | SOCS1 | CD3D | HSD11B1 | CPT1B | MCM4 | TPX2 | MCM4 | G6PD | GGT1 | CAT |
| 7 | CXCL1 | PGM1 | HMGCS1 | SKIL | POLR2C | CDC20 | PMAIP1 | FZD1 | CPT2 | SYBU | ⋯ | TNFRSF9 | CD28 | ETV4 | ECH1 | RRM1 | TOP2A | RRM1 | FTH1 | GGT5 | CCS |
| 8 | IER3 | NDRG1 | NSDHL | SKI | POLR2K | PLK1 | CASP8 | PSEN2 | CD36 | NPY1R | ⋯ | XBP1 | LYN | GLRX | ECHS1 | UNG | NDC80 | UNG | EPHX1 | GGT6 | CLIC2 |
| 9 | CD83 | HK2 | LSS | ACVR1 | GTF2H3 | TTK | JUN | FZD7 | GPAM | PDZK1 | ⋯ | RRAGD | HCLS1 | DUSP6 | HADHB | GINS2 | CKS2 | GINS2 | DDC | GGT7 | CYGB |
| 10 | CCL20 | ALDOC | MVD | PMEPA1 | POLR2D | PRC1 | BCL2L11 | DTX1 | ADIPOR2 | NRIP1 | ⋯ | HK2 | IL18 | SCG5 | PPARA | MCM6 | NUF2 | MCM6 | CBR3 | GPX1 | DUOX1 |
| 11 | CXCL3 | GPI | LDLR | NCOR2 | ERCC3 | NDC80 | MCL1 | DLL1 | ACAA2 | MLPH | ⋯ | PHLDA1 | CRTAM | ETV5 | PPARD | CDCA7 | CKS1B | CDCA7 | UIP1 | GPX2 | DUOX2 |
| 12 | MAFF | MXI1 | TM7SF2 | SERPINE1 | DDB2 | KIF11 | IL1B | FZD5 | ETFB | HSPB8 | ⋯ | IL2RB | IFNG | ITGB2 | CPT1C | DTL | MKI67 | DTL | UCHL1 | GPX3 | EPX |
| 13 | NFKB2 | SLC2A1 | ALDOC | JUNB | POLR1C | NUSAP1 | SPTAN1 | MAML2 | ACOX1 | EGR3 | ⋯ | CTLA4 | CD3G | AKT2 | NA | PRIM1 | TMPO | PRIM1 | TXN | GPX4 | FABP1 |
| 14 | TNFAIP2 | P4HA1 | EBP | SMAD1 | XPC | CKS2 | DIABLO | NOTCH1 | ACADM | KRT19 | ⋯ | NFIL3 | CD86 | PPBP | NA | UHRF1 | CENPF | UHRF1 | TSPAN7 | GPX5 | GPX1 |
| 15 | HBEGF | ADM | SCD | SMAD6 | PCNA | KIF2C | BAX | PSENEN | HADH | LRIG1 | ⋯ | CD83 | IL10 | G0S2 | NA | MLF1IP | TACC3 | MLF1IP | TRIM16 | GPX6 | GPX2 |
| 16 | KLF6 | P4HA2 | PMVK | PPP1R15A | POLR2I | MKI67 | BIK | WNT5A | IDH1 | KDM4B | ⋯ | IKZF2 | UBE2N | GABRA3 | NA | HELLS | FAM64A | HELLS | TM4SF20 | GPX7 | GPX3 |
| 17 | BIRC3 | ENO1 | MVK | TGIF1 | SUPT4H1 | AURKB | IL1A | CUL1 | SORBS1 | PGR | ⋯ | IL10 | BCL10 | IRF8 | NA | RFC2 | SMC4 | RFC2 | TGFB2 | GSR | GPX4 |
| 18 | PLAUR | PFKP | LPL | FURIN | POLD3 | TPX2 | BID | WNT2 | ACADS | RHOBTB3 | ⋯ | TNFRSF18 | CD4 | BIRC3 | NA | RPA2 | CCNB2 | RPA2 | TFRC | GSS | GPX5 |
| 19 | ZFP36 | AK4 | SC5D | SMAD3 | POLR3GL | SMC4 | CDKN1A | DTX4 | UCK1 | TPD52L1 | ⋯ | DHRS3 | LCK | FGF9 | NA | NASP | CKAP2L | NASP | TALDO1 | GSTA1 | GPX6 |
| 20 | ICAM1 | FAM162A | FADS2 | FKBP1A | POLR3C | BUB1 | GADD45A | SAP30 | SCP2 | ELOVL2 | ⋯ | ECM1 | NCK1 | DCBLD2 | NA | RAD51AP1 | CKAP2 | RAD51AP1 | SQSTM1 | GSTA2 | GPX7 |
| 21 | JUN | PFKFB3 | HMGCR | MAP3K7 | GTF2B | CENPF | DDIT3 | PPARD | DECR1 | RET | ⋯ | ADAM19 | C2 | INHBA | NA | GMNN | AURKB | GMNN | SPP1 | GSTA3 | GPX8 |
| 22 | EGR3 | VEGFA | HSD17B7 | BMPR1A | POLR1D | RACGAP1 | CDKN1B | KAT2A | CDKN2C | TPBG | ⋯ | SLC2A3 | HLA-A | TFPI | NA | WDR76 | BUB1 | WDR76 | SPANXB1 | GSTA4 | GSR |
| 23 | IL1B | BNIP3L | ANXA13 | CTNNB1 | NCBP2 | CENPE | TNF | HEYL | TALDO1 | TFF1 | ⋯ | HIPK2 | ITGB2 | TSPAN1 | NA | SLBP | KIF11 | SLBP | SPANXA1 | GSTA5 | GSTA1 |
| 24 | BCL2A1 | TPI1 | SREBF2 | HIPK2 | NELFE | AC027237.1 | GSN | SKP1 | TST | MAPT | ⋯ | BATF3 | HLA-DQA1 | ADAM8 | NA | CCNE2 | ANP32E | CCNE2 | SORD | GSTK1 | GSTK1 |
| 25 | PPP1R15A | ERO1A | PCYT2 | KLF10 | GTF2F1 | UBE2C | TNFSF10 | RBX1 | MCCC1 | SCNN1A | ⋯ | BHLHE40 | CD1D | SLPI | NA | UBR7 | TUBB4B | UBR7 | SLC7A11 | GSTM1 | GSTM2 |
| 26 | ZC3H12A | KDM3A | ACSS2 | BMP2 | ERCC5 | MCM6 | CASP6 | TCF7L2 | PGM1 | ABAT | ⋯ | PTGER2 | CD80 | PRKG2 | NA | POLD3 | GTSE1 | POLD3 | SLC6A6 | GSTM2 | GSTO1 |
| 27 | SOD2 | CCNG2 | ATF3 | ENG | LIG1 | MCM3 | SQSTM1 | ARRB1 | REEP5 | FLNB | ⋯ | DENND5A | HLA-DRA | MMP11 | NA | MSH2 | KIF20B | MSH2 | SLC38A6 | GSTM3 | GSTO2 |
| 28 | NR4A2 | LDHA | ADH4 | APC | ERCC1 | PTTG1 | FASLG | LFNG | BCL2L13 | XBP1 | ⋯ | ITIH5 | THY1 | MMP10 | NA | ATAD2 | HJURP | ATAD2 | SFN | GSTM4 | GSTP1 |
| 29 | IL1A | GYS1 | ETHE1 | PPM1A | ERCC4 | CDK1 | EGR3 | PRKCA | SLC25A10 | CELSR2 | ⋯ | PHTF2 | TLR1 | TMEM158 | NA | RAD51 | CDCA3 | RAD51 | SERPINE1 | GSTM5 | GSTT1 |
| 30 | RELB | GAPDH | ECH1 | XIAP | POLD4 | KIF4A | CD44 | DTX2 | ME1 | RAB31 | ⋯ | GADD45B | HLA-G | TNFAIP3 | NA | RRM2 | HN1 | RRM2 | RRM2 | GSTO1 | GSTZ1 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 171 | IL12B | MYH9 | NA | NA | NA | HMGN2 | NA | NA | DHRS7B | INHBB | ⋯ | UMPS | RIPK2 | FUCA1 | NA | NA | NA | NA | NA | NA | NA |
| 172 | IL6ST | CDKN1C | NA | NA | NA | SRSF10 | NA | NA | MIGA2 | BHLHE40 | ⋯ | HUWE1 | IKBKB | PLVAP | NA | NA | NA | NA | NA | NA | NA |
| 173 | SLC16A6 | GRHPR | NA | NA | NA | SNRPD1 | NA | NA | MGLL | CALB2 | ⋯ | COL6A1 | GCNT1 | ADAM17 | NA | NA | NA | NA | NA | NA | NA |
| 174 | ABCA1 | PCK1 | NA | NA | NA | CASP8AP2 | NA | NA | ITSN1 | FASN | ⋯ | ABCB1 | SOCS5 | AVL9 | NA | NA | NA | NA | NA | NA | NA |
| 175 | HES1 | INHA | NA | NA | NA | SMARCC1 | NA | NA | DHCR7 | CHPT1 | ⋯ | RNH1 | IRF8 | ADAMDEC1 | NA | NA | NA | NA | NA | NA | NA |
| 176 | BCL6 | HSPA5 | NA | NA | NA | SLC12A2 | NA | NA | RREB1 | MYBBP1A | ⋯ | IRF8 | TAP2 | HKDC1 | NA | NA | NA | NA | NA | NA | NA |
| 177 | IRS2 | NDST2 | NA | NA | NA | NOTCH2 | NA | NA | CMBL | ELOVL5 | ⋯ | GUCY1B1 | EIF4G3 | MAP7 | NA | NA | NA | NA | NA | NA | NA |
| 178 | SLC2A3 | NEDD4L | NA | NA | NA | TNPO2 | NA | NA | UBC | DYNLT3 | ⋯ | AHCY | ABI1 | IL7R | NA | NA | NA | NA | NA | NA | NA |
| 179 | CEBPD | TPBG | NA | NA | NA | SMAD3 | NA | NA | ATP5PO | ABLIM1 | ⋯ | PRAF2 | CCL7 | RBM4 | NA | NA | NA | NA | NA | NA | NA |
| 180 | IL23A | XPNPEP1 | NA | NA | NA | MAP3K20 | NA | NA | PRDX3 | SOX3 | ⋯ | GSTO1 | IL2RB | BPGM | NA | NA | NA | NA | NA | NA | NA |
| 181 | SMAD3 | IL6 | NA | NA | NA | HSPA8 | NA | NA | DBT | SLC24A3 | ⋯ | TWSG1 | BRCA1 | ENG | NA | NA | NA | NA | NA | NA | NA |
| 182 | TAP1 | SLC6A6 | NA | NA | NA | G3BP1 | NA | NA | NDUFS3 | RAB17 | ⋯ | CDC42SE2 | FGR | GFPT2 | NA | NA | NA | NA | NA | NA | NA |
| 183 | MSC | MAP3K1 | NA | NA | NA | PTTG3P | NA | NA | NKIRAS1 | MAST4 | ⋯ | PLAGL1 | IL18RAP | PLAU | NA | NA | NA | NA | NA | NA | NA |
| 184 | IFIH1 | LDHC | NA | NA | NA | DMD | NA | NA | RAB34 | KCNK5 | ⋯ | APLP1 | MRPL3 | GNG11 | NA | NA | NA | NA | NA | NA | NA |
| 185 | IL15RA | AKAP12 | NA | NA | NA | MEIS1 | NA | NA | CIDEA | ELF1 | ⋯ | AKAP2 | CXCL13 | PTCD2 | NA | NA | NA | NA | NA | NA | NA |
| 186 | TNIP2 | TES | NA | NA | NA | HNRNPU | NA | NA | UQCRQ | RPS6KA2 | ⋯ | PLPP1 | CAPG | MAP3K1 | NA | NA | NA | NA | NA | NA | NA |
| 187 | BCL3 | KIF5A | NA | NA | NA | SRSF2 | NA | NA | PEX14 | ISG20L2 | ⋯ | SPRY4 | EIF5A | CBL | NA | NA | NA | NA | NA | NA | NA |
| 188 | PANX1 | LALBA | NA | NA | NA | MT2A | NA | NA | BCL6 | IL6ST | ⋯ | SCN9A | RPS3A | CXCR4 | NA | NA | NA | NA | NA | NA | NA |
| 189 | FJX1 | COL5A1 | NA | NA | NA | NUP98 | NA | NA | COX6A1 | SYNGR1 | ⋯ | SHE | GALNT1 | NIN | NA | NA | NA | NA | NA | NA | NA |
| 190 | EDN1 | GPC1 | NA | NA | NA | EWSR1 | NA | NA | DNAJB9 | SH3BP5 | ⋯ | PDCD2L | ST8SIA4 | IKZF1 | NA | NA | NA | NA | NA | NA | NA |
| 191 | EIF1 | HDLBP | NA | NA | NA | KIF5B | NA | NA | MAP4K3 | ALDH3B1 | ⋯ | CCND3 | CCL13 | WDR33 | NA | NA | NA | NA | NA | NA | NA |
| 192 | BMP2 | ILVBL | NA | NA | NA | MTF2 | NA | NA | ANGPT1 | THSD4 | ⋯ | LRIG1 | RPL3L | MYCN | NA | NA | NA | NA | NA | NA | NA |
| 193 | DUSP4 | NCAN | NA | NA | NA | E2F4 | NA | NA | UBQLN1 | CLIC3 | ⋯ | SWAP70 | LY75 | FCER1G | NA | NA | NA | NA | NA | NA | NA |
| 194 | PDLIM5 | TGM2 | NA | NA | NA | BCL3 | NA | NA | NDUFB7 | NXT1 | ⋯ | SLC39A8 | TAPBP | PECAM1 | NA | NA | NA | NA | NA | NA | NA |
| 195 | ICOSLG | ETS1 | NA | NA | NA | PURA | NA | NA | SLC19A1 | NAV2 | ⋯ | RABGAP1L | NOS2 | CCSER2 | NA | NA | NA | NA | NA | NA | NA |
| 196 | GFPT2 | HOXB9 | NA | NA | NA | MEIS2 | NA | NA | ABCB8 | RRP12 | ⋯ | TGM2 | RPL9 | SNAP91 | NA | NA | NA | NA | NA | NA | NA |
| 197 | KLF2 | SELENBP1 | NA | NA | NA | PAFAH1B1 | NA | NA | PQLC3 | ADCY1 | ⋯ | PNP | BCAT1 | EVI5 | NA | NA | NA | NA | NA | NA | NA |
| 198 | TNC | FOSL2 | NA | NA | NA | WRN | NA | NA | POR | DHCR7 | ⋯ | AGER | IL9 | TNFRSF1B | NA | NA | NA | NA | NA | NA | NA |
| 199 | SERPINB8 | SULT2B1 | NA | NA | NA | H2AFV | NA | NA | UCP2 | MICB | ⋯ | ETV4 | IL27RA | GPNMB | NA | NA | NA | NA | NA | NA | NA |
| 200 | MXD1 | TGFB3 | NA | NA | NA | ODF2 | NA | NA | UQCR11 | AKAP1 | ⋯ | CD86 | DYRK3 | TPH1 | NA | NA | NA | NA | NA | NA | NA |
In [125]:
drivers = as.data.frame(drivers)
for (pw in colnames(pathway_hallmark)){
drivers[pw] = drivers$V1 %in% pathway_hallmark[,pw]
}
In [128]:
drivers
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank | ⋯ | HALLMARK_IL2_STAT5_SIGNALING | HALLMARK_ALLOGRAFT_REJECTION | HALLMARK_KRAS_SIGNALING_UP | FATTY_ACID_BETA_OXIDATION | S.GENES.ITAY | G2M.GENES.ITAY | CELL_CYCLE | NRF2_ROMORO | KEGG_GLUTATHIONE_METABOLISM | GO_ANTIOXIDANT_ACTIVITY |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <int> | ⋯ | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> |
| FN1 | 0.007993378 | 0.0007537607 | 1.907695e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 1 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| HIST1H2BD | 0.003864827 | 0.0005008594 | 1.813786e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 2 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.002306744 | 0.0002728280 | 1.020445e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 3 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| KRT8 | 0.004171373 | 0.0006077977 | 7.349687e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 4 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.006570227 | 0.0004776801 | 1.470234e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 5 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |
| CD9 | 0.004286982 | 0.0004431204 | 1.179431e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 6 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE | 7 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| LAPTM4A | 0.005557391 | 0.0006989103 | 1.191966e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 8 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| MDK | 0.004485494 | 0.0007073663 | 9.334373e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 9 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| PRDX1 | 0.005753293 | 0.0005948305 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 10 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE |
| PGK1 | 0.002937007 | 0.0004105406 | 8.659618e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 11 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| NQO1 | 0.007189172 | 0.0005834166 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 12 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE |
| ALPP | 0.003790364 | 0.0003220504 | 1.560096e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 13 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| MT2A | 0.002992815 | 0.0007070334 | -5.982608e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 14 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| PKM | 0.002418476 | 0.0004418046 | 8.013423e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 15 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| UBB | 0.002093029 | 0.0004443382 | -5.899295e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 16 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| FOSL1 | 0.004265769 | 0.0005058976 | -9.429040e-05 | 10 | TRUE | FALSE | FALSE | FALSE | 17 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CTSA | 0.004027971 | 0.0005851937 | 1.295741e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 18 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| UBE2S | 0.004784057 | 0.0005292148 | -1.516910e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 19 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| HSPB1 | 0.002017296 | 0.0003566292 | 7.373099e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 20 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| MYL6 | 0.008508458 | 0.0008462955 | 1.682113e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 21 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| TCOF1 | 0.002286529 | 0.0003146553 | -6.928714e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 22 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| TFDP1 | 0.003330428 | 0.0003013794 | -9.927206e-05 | 10 | TRUE | FALSE | FALSE | FALSE | 23 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CSTB | 0.003148289 | 0.0003750370 | -1.198390e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 24 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| JUND | 0.001684324 | 0.0003056773 | 1.057126e-04 | 10 | TRUE | FALSE | FALSE | FALSE | 25 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| TGM2 | 0.005329582 | 0.0004317851 | -1.178054e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 26 | ⋯ | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| SQSTM1 | 0.002869604 | 0.0004758771 | 9.440278e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 27 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE |
| HIST1H4C | 0.003751588 | 0.0004830967 | -1.076414e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 28 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CKS1B | 0.002607002 | 0.0003412548 | 9.403953e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 29 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE |
| CD24 | 0.002727851 | 0.0002715580 | 1.166907e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 30 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| CENPN | 0.0021267512 | 3.911722e-04 | 8.123492e-05 | 8 | FALSE | FALSE | FALSE | FALSE | 34 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| HMGA1 | 0.0012568032 | 5.449500e-04 | -4.842230e-05 | 7 | TRUE | FALSE | FALSE | FALSE | 35 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CD81 | 0.0026813797 | 2.755875e-04 | 1.560641e-04 | 7 | FALSE | FALSE | FALSE | FALSE | 36 | ⋯ | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| PPIF | 0.0013978999 | 1.616978e-04 | -1.086447e-05 | 6 | FALSE | FALSE | FALSE | FALSE | 37 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| PSMB6 | 0.0015816240 | 1.423927e-04 | 9.055104e-05 | 5 | FALSE | FALSE | FALSE | FALSE | 38 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CDK1 | 0.0007892986 | 1.556827e-04 | -6.600696e-05 | 5 | FALSE | FALSE | FALSE | FALSE | 39 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE |
| CCNB1 | 0.0009600252 | 7.355914e-05 | 3.018131e-07 | 5 | FALSE | FALSE | FALSE | FALSE | 40 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| SERPINH1 | 0.0011331889 | 2.452786e-04 | 5.132065e-05 | 5 | FALSE | FALSE | FALSE | FALSE | 41 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| FOXM1 | 0.0006686676 | 1.551108e-04 | -7.297643e-06 | 5 | TRUE | FALSE | FALSE | FALSE | 42 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| LAMP1 | 0.0014675947 | 3.839450e-04 | 7.594953e-05 | 5 | FALSE | FALSE | FALSE | FALSE | 43 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| PPP1R14B | 0.0011937528 | 1.994650e-04 | -8.283027e-06 | 4 | FALSE | FALSE | FALSE | FALSE | 44 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| TFPI2 | 0.0011605380 | 2.944522e-04 | 7.088958e-05 | 4 | FALSE | FALSE | FALSE | FALSE | 45 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| ALDH3A1 | 0.0013055377 | 1.966169e-04 | 4.354592e-07 | 4 | FALSE | TRUE | FALSE | TRUE | 46 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE |
| CALD1 | 0.0006721359 | 9.202680e-06 | 3.342999e-07 | 3 | FALSE | FALSE | FALSE | FALSE | 47 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| TUBA4A | 0.0005829521 | 1.696022e-04 | 1.596368e-07 | 3 | FALSE | FALSE | FALSE | FALSE | 48 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| IGFBP3 | 0.0010258654 | 2.486552e-04 | 1.015227e-05 | 3 | FALSE | FALSE | FALSE | FALSE | 49 | ⋯ | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| TUBB4B | 0.0003830904 | 4.519722e-05 | 7.412989e-08 | 2 | FALSE | FALSE | FALSE | FALSE | 50 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE |
| MYBL2 | 0.0002402041 | 1.968738e-04 | 6.863686e-06 | 2 | TRUE | FALSE | FALSE | FALSE | 51 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| PHGDH | 0.0004173670 | 5.805091e-05 | 2.442747e-07 | 2 | FALSE | FALSE | FALSE | FALSE | 52 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| UBC | 0.0002102067 | 6.868151e-05 | 1.274175e-07 | 2 | FALSE | FALSE | FALSE | FALSE | 53 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| PCNA | 0.0004494411 | 1.240919e-04 | 5.007451e-05 | 2 | FALSE | FALSE | FALSE | FALSE | 54 | ⋯ | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE |
| HIST1H1C | 0.0004837657 | 1.441257e-04 | 9.379254e-06 | 2 | FALSE | FALSE | FALSE | FALSE | 55 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CTNNB1 | 0.0001381328 | 0.000000e+00 | -9.900118e-09 | 1 | FALSE | FALSE | FALSE | FALSE | 56 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CALR | 0.0002454971 | 0.000000e+00 | -1.203399e-07 | 1 | FALSE | FALSE | FALSE | FALSE | 57 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| MCM3 | 0.0001789002 | 0.000000e+00 | 2.958242e-10 | 1 | FALSE | FALSE | FALSE | FALSE | 58 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| CENPF | 0.0002376977 | 0.000000e+00 | 6.111640e-09 | 1 | FALSE | FALSE | FALSE | FALSE | 59 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE |
| ANXA2 | 0.0001817979 | 0.000000e+00 | -2.258579e-10 | 1 | FALSE | FALSE | FALSE | FALSE | 60 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |
| TOP2A | 0.0002693551 | 0.000000e+00 | -8.096725e-10 | 1 | FALSE | FALSE | FALSE | FALSE | 61 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE |
| NDC80 | 0.0002256619 | 0.000000e+00 | -6.941362e-08 | 1 | FALSE | FALSE | FALSE | FALSE | 62 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE |
| SMC4 | 0.0001939561 | 0.000000e+00 | -6.988958e-08 | 1 | FALSE | FALSE | FALSE | FALSE | 63 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE |
In [131]:
drivers[drivers$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank | ⋯ | HALLMARK_IL2_STAT5_SIGNALING | HALLMARK_ALLOGRAFT_REJECTION | HALLMARK_KRAS_SIGNALING_UP | FATTY_ACID_BETA_OXIDATION | S.GENES.ITAY | G2M.GENES.ITAY | CELL_CYCLE | NRF2_ROMORO | KEGG_GLUTATHIONE_METABOLISM | GO_ANTIOXIDANT_ACTIVITY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <int> | ⋯ | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | |
| 10 | PRDX1 | 0.005753293 | 0.0005948305 | 0.0001307083 | 10 | FALSE | FALSE | TRUE | TRUE | 10 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE |
| 12 | NQO1 | 0.007189172 | 0.0005834166 | 0.0001745988 | 10 | FALSE | FALSE | TRUE | TRUE | 12 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE |
In [156]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [157]:
drivers[drivers$is_in_ROS,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| PRDX1 | 0.005753293 | 0.0005948305 | 0.0001307083 | 10 | FALSE | FALSE | TRUE | TRUE | 5 | 9 |
| NQO1 | 0.007189172 | 0.0005834166 | 0.0001745988 | 10 | FALSE | FALSE | TRUE | TRUE | 3 | 3 |
In [146]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank | ⋯ | HALLMARK_ALLOGRAFT_REJECTION | HALLMARK_KRAS_SIGNALING_UP | FATTY_ACID_BETA_OXIDATION | S.GENES.ITAY | G2M.GENES.ITAY | CELL_CYCLE | NRF2_ROMORO | KEGG_GLUTATHIONE_METABOLISM | GO_ANTIOXIDANT_ACTIVITY | rank_shap_weight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <int> | ⋯ | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | |
| 7 | H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE | 7 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | 37 |
| 10 | PRDX1 | 0.005753293 | 0.0005948305 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 10 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | 5 |
| 12 | NQO1 | 0.007189172 | 0.0005834166 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 12 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | 3 |
| 46 | ALDH3A1 | 0.001305538 | 0.0001966169 | 4.354592e-07 | 4 | FALSE | TRUE | FALSE | TRUE | 46 | ⋯ | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | 39 |
In [ ]:
In [174]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t7_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [175]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| BIN1 | 0.003354605 | 0.0003882261 | 0.0005153501 | 10 | FALSE | FALSE | FALSE | FALSE |
| CKS1B | 0.002709994 | 0.0005753519 | 0.0004540569 | 10 | FALSE | FALSE | FALSE | FALSE |
| PRDX1 | 0.003196059 | 0.0005124655 | 0.0004087128 | 10 | FALSE | FALSE | TRUE | TRUE |
| KRT8 | 0.003889918 | 0.0002229171 | 0.0005481308 | 10 | FALSE | FALSE | FALSE | FALSE |
| SRSF3 | 0.002838213 | 0.0003422896 | 0.0004343811 | 10 | FALSE | FALSE | FALSE | FALSE |
| TPM1 | 0.002373309 | 0.0003037851 | -0.0005296845 | 10 | FALSE | FALSE | FALSE | FALSE |
In [176]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [177]:
drivers[drivers$is_in_ROS,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| PRDX1 | 0.003196059 | 0.0005124655 | 0.0004087128 | 10 | FALSE | FALSE | TRUE | TRUE | 13 | 22 |
| NQO1 | 0.002075033 | 0.0002167071 | 0.0003687808 | 9 | FALSE | FALSE | TRUE | TRUE | 23 | 24 |
In [178]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| PRDX1 | 0.003196059 | 0.0005124655 | 0.0004087128 | 10 | FALSE | FALSE | TRUE | TRUE | 13 | 22 |
| H2AFZ | 0.004075658 | 0.0005847643 | 0.0006475787 | 10 | FALSE | TRUE | FALSE | TRUE | 8 | 8 |
| ALDH3A1 | 0.004452039 | 0.0005462090 | 0.0007624793 | 10 | FALSE | TRUE | FALSE | TRUE | 6 | 5 |
| NQO1 | 0.002075033 | 0.0002167071 | 0.0003687808 | 9 | FALSE | FALSE | TRUE | TRUE | 23 | 24 |
In [164]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t14_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [165]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| HLA-B | 0.006611837 | 0.0014072035 | -0.003888358 | 10 | FALSE | FALSE | FALSE | FALSE |
| H2AFZ | 0.001543479 | 0.0003817574 | 0.001596191 | 10 | FALSE | TRUE | FALSE | TRUE |
| UBC | 0.003014871 | 0.0006034041 | -0.003552045 | 10 | FALSE | FALSE | FALSE | FALSE |
| IGFBP5 | 0.011728111 | 0.0037999116 | 0.013004079 | 10 | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.009201345 | 0.0016104131 | 0.004694919 | 10 | FALSE | FALSE | FALSE | FALSE |
| SAT1 | 0.003824356 | 0.0007941552 | -0.001987261 | 10 | FALSE | FALSE | FALSE | FALSE |
In [166]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [167]:
drivers[drivers$is_in_ROS,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| JUNB | 0.0016835371 | 0.0008096141 | -0.0029814138 | 7 | TRUE | FALSE | TRUE | TRUE | 20 | 9 |
| SOD2 | 0.0002215576 | 0.0000000000 | 0.0007589676 | 1 | FALSE | FALSE | TRUE | TRUE | 63 | 39 |
| PFKP | 0.0002421256 | 0.0000000000 | 0.0002091714 | 1 | FALSE | FALSE | TRUE | TRUE | 61 | 63 |
In [168]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| H2AFZ | 0.0015434791 | 0.0003817574 | 0.0015961908 | 10 | FALSE | TRUE | FALSE | TRUE | 22 | 20 |
| JUNB | 0.0016835371 | 0.0008096141 | -0.0029814138 | 7 | TRUE | FALSE | TRUE | TRUE | 20 | 9 |
| SOD2 | 0.0002215576 | 0.0000000000 | 0.0007589676 | 1 | FALSE | FALSE | TRUE | TRUE | 63 | 39 |
| LGALS1 | 0.0001387697 | 0.0000000000 | 0.0001830989 | 1 | FALSE | TRUE | FALSE | TRUE | 72 | 65 |
| PFKP | 0.0002421256 | 0.0000000000 | 0.0002091714 | 1 | FALSE | FALSE | TRUE | TRUE | 61 | 63 |
t0_14¶
In [163]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [164]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| FN1 | 0.007993378 | 0.0007537607 | 1.907695e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| HIST1H2BD | 0.003864827 | 0.0005008594 | 1.813786e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.002306744 | 0.0002728280 | 1.020445e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| KRT8 | 0.004171373 | 0.0006077977 | 7.349687e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.006570227 | 0.0004776801 | 1.470234e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD9 | 0.004286982 | 0.0004431204 | 1.179431e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
In [767]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [768]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE | 37 | 41 |
| PRDX1 | 0.005753293 | 0.0005948305 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 5 | 9 |
| NQO1 | 0.007189172 | 0.0005834166 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 3 | 3 |
| ALDH3A1 | 0.001305538 | 0.0001966169 | 4.354592e-07 | 4 | FALSE | TRUE | FALSE | TRUE | 39 | 49 |
In [283]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [286]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [287]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [288]:
plot_df
| Gene | variable | value |
|---|---|---|
| <chr> | <chr> | <dbl> |
| ACTG2 | run0 | 0.0020976764 |
| ALDH3A1 | run0 | 0.0030892894 |
| ALPP | run0 | 0.0034531571 |
| ANXA2 | run0 | NA |
| APP | run0 | NA |
| ASF1B | run0 | NA |
| ATF3 | run0 | 0.0003612373 |
| AURKA | run0 | 0.0015353375 |
| AURKB | run0 | NA |
| CALD1 | run0 | NA |
| CALR | run0 | NA |
| CCNA2 | run0 | NA |
| CCNB1 | run0 | NA |
| CCND1 | run0 | 0.0026891035 |
| CD24 | run0 | 0.0028103908 |
| CD81 | run0 | 0.0037515941 |
| CD9 | run0 | 0.0041851407 |
| CDC20 | run0 | 0.0008733733 |
| CDK1 | run0 | 0.0011039842 |
| CEBPB | run0 | NA |
| CEBPD | run0 | NA |
| CENPA | run0 | NA |
| CENPF | run0 | NA |
| CENPM | run0 | NA |
| CENPN | run0 | 0.0030142995 |
| CENPV | run0 | NA |
| CKS1B | run0 | NA |
| CSTB | run0 | 0.0027676788 |
| CTNNB1 | run0 | NA |
| CTSA | run0 | 0.0037647824 |
| ⋮ | ⋮ | ⋮ |
| PGK1 | run9 | 0.002112776 |
| PHGDH | run9 | 0.001609636 |
| PKM | run9 | 0.002349730 |
| PLK1 | run9 | NA |
| PPIF | run9 | 0.001792963 |
| PPP1R14B | run9 | 0.002269739 |
| PRDX1 | run9 | 0.005420250 |
| PSAP | run9 | NA |
| PSMB6 | run9 | 0.002708103 |
| RAN | run9 | NA |
| RANBP1 | run9 | 0.003856269 |
| RFC4 | run9 | NA |
| SERPINH1 | run9 | 0.001784495 |
| SMC4 | run9 | NA |
| SQSTM1 | run9 | 0.003209935 |
| STAT1 | run9 | NA |
| TCOF1 | run9 | 0.002694048 |
| TFDP1 | run9 | 0.003229773 |
| TFPI2 | run9 | NA |
| TGM2 | run9 | 0.005533601 |
| TOP2A | run9 | 0.002693551 |
| TUBA1A | run9 | 0.002486760 |
| TUBA1B | run9 | 0.000902929 |
| TUBA4A | run9 | NA |
| TUBB4B | run9 | 0.001672432 |
| TYMS | run9 | NA |
| UBB | run9 | 0.001767165 |
| UBC | run9 | NA |
| UBE2S | run9 | 0.004712779 |
| UCHL1 | run9 | NA |
In [289]:
pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
In [324]:
plot_df$pw = plot_df$Gene %in% pathway_hallmark$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY
In [291]:
pathway_hallmark$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY
- 'ABCC1'
- 'ATOX1'
- 'CAT'
- 'CDKN2D'
- 'EGLN2'
- 'ERCC2'
- 'FES'
- 'FTL'
- 'G6PD'
- 'GCLC'
- 'GCLM'
- 'GLRX'
- 'GLRX2'
- 'GPX3'
- 'GPX4'
- 'GSR'
- 'HHEX'
- 'HMOX2'
- 'IPCEF1'
- 'JUNB'
- 'LAMTOR5'
- 'LSP1'
- 'MBP'
- 'MGST1'
- 'MPO'
- 'MSRA'
- 'NDUFA6'
- 'NDUFB4'
- 'NDUFS2'
- 'NQO1'
- 'OXSR1'
- 'PDLIM1'
- 'PFKP'
- 'PRDX1'
- 'PRDX2'
- 'PRDX4'
- 'PRDX6'
- 'PRNP'
- 'PTPA'
- 'SBNO2'
- 'SCAF4'
- 'SELENOS'
- 'SOD1'
- 'SOD2'
- 'SRXN1'
- 'STK25'
- 'TXN'
- 'TXNRD1'
- 'TXNRD2'
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
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- NA
- NA
- NA
- NA
- NA
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- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
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- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
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- NA
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- NA
- NA
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In [292]:
pathway_hallmark$HALLMARK_FATTY_ACID_METABOLISM
- 'ACAA1'
- 'ACAA2'
- 'ACADL'
- 'ACADM'
- 'ACOT8'
- 'ACOX1'
- 'ACSL1'
- 'ALDH3A2'
- 'CCDC58'
- 'CPT2'
- 'CYP4A11'
- 'DECR1'
- 'ECH1'
- 'ECI1'
- 'ELOVL5'
- 'FABP1'
- 'FABP2'
- 'HADH'
- 'HIBCH'
- 'HMGCL'
- 'HSD17B11'
- 'IDH1'
- 'ME1'
- 'MGLL'
- 'MLYCD'
- 'PCBD1'
- 'RETSAT'
- 'S100A10'
- 'SUCLG1'
- 'VNN1'
- 'EHHADH'
- 'ALDH9A1'
- 'HADHB'
- 'ECHS1'
- 'ACADS'
- 'CA2'
- 'HSD17B10'
- 'ALDH1A1'
- 'ACADVL'
- 'HSD17B4'
- 'CA4'
- 'ADH1C'
- 'ADH7'
- 'PTS'
- 'MAOA'
- 'HAO2'
- 'HSD17B7'
- 'MCEE'
- 'ACAT2'
- 'AUH'
- 'HPGD'
- 'FH'
- 'HMGCS2'
- 'ALAD'
- 'GPD1'
- 'ACO2'
- 'CBR1'
- 'GRHPR'
- 'ACOT2'
- 'G0S2'
- 'MDH2'
- 'HSP90AA1'
- 'BCKDHB'
- 'UROS'
- 'YWHAH'
- 'LDHA'
- 'CRYZ'
- 'RDH16'
- 'INMT'
- 'UGDH'
- 'GSTZ1'
- 'IDH3B'
- 'MDH1'
- 'CRAT'
- 'ETFDH'
- 'CD36'
- 'ECI2'
- 'SDHD'
- 'ACSL5'
- 'HSDL2'
- 'HMGCS1'
- 'SDHC'
- 'CD1D'
- 'GCDH'
- 'GPD2'
- 'ALDH3A1'
- 'SLC22A5'
- 'PDHB'
- 'TDO2'
- 'FASN'
- 'NBN'
- 'PSME1'
- 'PPARA'
- 'NCAPH2'
- 'BPHL'
- 'ODC1'
- 'CA6'
- 'DLD'
- 'HCCS'
- 'EPHX1'
- 'DLST'
- 'FMO1'
- 'AOC3'
- 'UROD'
- 'CPT1A'
- 'KMT5A'
- 'UBE2L6'
- 'MIF'
- 'SUCLG2'
- 'CPOX'
- 'SMS'
- 'CBR3'
- 'NTHL1'
- 'CIDEA'
- 'IDI1'
- 'AADAT'
- 'REEP6'
- 'HSPH1'
- 'APEX1'
- 'NSDHL'
- 'ADIPOR2'
- 'ACSL4'
- 'ACSS1'
- 'ENO3'
- 'IDH3G'
- 'LGALS1'
- 'METAP1'
- 'ALDOA'
- 'ACSM3'
- 'LTC4S'
- 'D2HGDH'
- 'ADSL'
- 'SUCLA2'
- 'SDHA'
- 'XIST'
- 'OSTC'
- 'GLUL'
- 'CYP4A22'
- 'GABARAPL1'
- 'AQP7'
- 'CYP1A1'
- 'PRDX6'
- 'ERP29'
- 'H2AFZ'
- 'GAPDHS'
- 'DHCR24'
- 'GAD2'
- 'PTPRG'
- 'IL4I1'
- 'TP53INP2'
- 'PDHA1'
- 'RAP1GDS1'
- 'CEL'
- 'BLVRA'
- 'SERINC1'
- 'BMPR1B'
- 'RDH11'
- 'ENO2'
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
- NA
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- NA
- NA
- NA
- NA
- NA
- NA
- NA
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- NA
- NA
- NA
- NA
In [293]:
ggplot(data = plot_df)+
geom_boxplot(aes(x=variable,y=value,fill=pw))+
theme_classic()
Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_boxplot()`).”
In [294]:
ggplot(data = plot_df)+
geom_violin(aes(x=variable,y=value,fill=pw))+
theme_classic()
Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_ydensity()`).”
In [325]:
plot_df$pw <- plot_df$pw %>% as.character %>% factor(levels = c('TRUE','FALSE'),labels = c('In two pathway','Out of two pathway'))
In [373]:
#ggplot2 | position_dodge位置调整函数https://blog.csdn.net/weixin_54000907/article/details/120108707
box_p <- ggplot(data = plot_df)+
stat_boxplot(aes(x=variable,y=value,color = pw),geom = "errorbar",linewidth=1.5,
position = position_dodge2(padding = 0.2))+
geom_boxplot(aes(x=variable,y=value,color = pw),outliers = F,linewidth=1.5,fill='white',position = position_dodge2(padding = 0.2))+
#geom_jitter(aes(x=variable,y=value,color = pw),width = 0.15)+
theme_classic()+
xlab('Run with different seeds')+
ylab('SHAP weight of driver genes')+
labs(fill="Genes Type")+
theme_bw()+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top'
)
box_p
Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_boxplot()`).” Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_boxplot()`).”
In [374]:
ggsave(plot = box_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_box_p.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = box_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_box_p.png',
width =16/1.5, height =16/1.5)
Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_boxplot()`).” Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_boxplot()`).” Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_boxplot()`).” Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_boxplot()`).”
In [375]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [376]:
tmp <- plot_df[plot_df$Gene %in% gl,]
tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [377]:
gl
- 'MYL6'
- 'FN1'
- 'NQO1'
- 'GSTM3'
- 'PRDX1'
- 'LAPTM4A'
- 'TGM2'
- 'UBE2S'
- 'MDK'
- 'CD9'
In [380]:
?ggsci::scale_fill_npg()
In [402]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
legend.position = 'none'
)
ridge_p
Picking joint bandwidth of 0.000309
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [403]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_ridge_p.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_ridge_p.png',
width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.000309 Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Picking joint bandwidth of 0.000309 Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.”
In [404]:
normalize <- function(v) {
(v - min(v)) / (max(v) - min(v))
}
In [405]:
drivers$norm_shap <- normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = ddf$V1)
In [408]:
ggplot(ddf) +
geom_bar(aes(x = V1, y = norm_shap),stat = "identity",fill=rgb(0.1,0.4,0.5,0.7))+
theme_classic()
In [414]:
drivers$norm_shap <- normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = rev(ddf$V1))
In [436]:
bar_p <- ggplot(ddf) +
geom_bar(aes(x = V1, y = norm_shap,fill=norm_shap),stat = "identity",width = 0.6, alpha=1)+
scale_fill_gradient(low = "#659999",high = "#f4791f")+
coord_flip() +
theme_bw()+
ylab('SHAP weight')+
xlab('Driver genes')+
ggtitle("Modeling between T0 T14")+
#scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
axis.text.y = element_text(colour = c(rep('black',times = 5),'#BA002B','black','#BA002B',rep('black',times = 2))),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(size = 1.5),
panel.grid = element_blank(),
panel.grid.minor.x = element_line(linetype = 5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
legend.position = 'none'
)
bar_p
Warning message:
“Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.”
In [439]:
ggsave(plot = bar_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_bar_p.pdf',
width =12/1.5, height =16/1.5)
ggsave(plot = bar_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_bar_p.png',
width =12/1.5, height =16/1.5)
In [ ]:
#R语言ggplot2图例标签、标题、顺序修改和删除https://blog.csdn.net/LeaningR/article/details/114576555
#ggplot2 美化 背景/主题(theme)https://zhuanlan.zhihu.com/p/463041897
p <- ggplot(data = plot_df)+
geom_bar(aes(x=ID,y=enrichmentScore,fill=-log10(pvalue)),stat="identity",width = 0.6, alpha=0.85)+
#ggsci::scale_fill_gsea()+
scale_fill_gradient(low = "#659999",high = "#f4791f")+
#scale_fill_gradient(low = "#8360c3",high = "#2ebf91")+
#scale_fill_gradient(low = "#636363",high = "#a2ab58")+
#scale_fill_gradient2(low = "#355C7D",mid = "#6C5B7B",high = "#C06C84",midpoint = 1.6)+
coord_flip() +
xlab('Hallmark gene sets')+
ylab('Enrichment Score')+
labs(fill = "-log10(P)")+
theme_bw()+
theme(panel.border = element_rect(size = 1.5),
panel.grid = element_blank(),
axis.ticks = element_line(size = 1),
axis.ticks.length = unit(5,'points'),
axis.text = element_text(face = 'bold',colour = 'black'),
axis.text.y = element_text(colour = c(rep('black',times = 4),'#BA002B','#BA002B')),
#axis.title.x = element_text(),
axis.title.y = element_blank()
)
p
In [175]:
for (i in drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:25]){
message(i)
}
MYL6 FN1 NQO1 GSTM3 PRDX1 LAPTM4A TGM2 UBE2S MDK CD9 FOSL1 KRT8 CTSA HIST1H2BD ALPP HIST1H4C TFDP1 CSTB MT2A PGK1 SQSTM1 CD24 RANBP1 CD81 CKS1B
In [5]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [6]:
head(drivers_exp)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | time0_exp | time3_exp | time7_exp | time14_exp |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> | <dbl> | <dbl> |
| MYL6 | 0.008508458 | 0.0008462955 | 0.0001682113 | 10 | FALSE | FALSE | FALSE | FALSE | -1.2952663 | -0.1738619 | -0.5429639 | 0.5844932 |
| FN1 | 0.007993378 | 0.0007537607 | 0.0001907695 | 10 | FALSE | FALSE | FALSE | FALSE | -0.7126312 | -0.2556758 | -0.0774729 | 0.3388334 |
| NQO1 | 0.007189172 | 0.0005834166 | 0.0001745988 | 10 | FALSE | FALSE | TRUE | TRUE | -0.4633937 | -0.6412671 | -0.3941414 | 0.5786879 |
| GSTM3 | 0.006570227 | 0.0004776801 | 0.0001470234 | 10 | FALSE | FALSE | FALSE | FALSE | -0.4995057 | -0.4245171 | -0.5519654 | 0.5208334 |
| PRDX1 | 0.005753293 | 0.0005948305 | 0.0001307083 | 10 | FALSE | FALSE | TRUE | TRUE | -0.7079641 | -0.5385526 | -0.6459265 | 0.6627243 |
| LAPTM4A | 0.005557391 | 0.0006989103 | 0.0001191966 | 10 | FALSE | FALSE | FALSE | FALSE | -1.1327481 | -0.2266731 | -0.4534626 | 0.5443161 |
In [210]:
drivers_exp_long<-drivers_exp %>%as.data.frame %>%
mutate(direction=ifelse(weight_grad_total_dir_mean > 0,'pos','neg')) %>%
mutate(rank=rank(-drivers_exp$weight_shap_total_mean)) %>%
mutate(top20 = ifelse(rank<=20,TRUE,FALSE)) %>%
mutate(top10 = ifelse(rank<=10,TRUE,FALSE)) %>%
dplyr::select(c('V1','time0_exp','time3_exp','time7_exp','time14_exp','direction','top20','top10')) %>%
reshape2::melt(id.vars = c("V1",'direction','top20','top10'), #需保留的不参与聚合的变量列名
measure.vars = c('time0_exp','time3_exp','time7_exp','time14_exp'),#需要聚合的变量s1-s10
variable.name = c('time_point'),#聚合变量的新列名
value.name = 'exp')#聚合值的新列名
In [211]:
head(drivers_exp_long)
| V1 | direction | top20 | top10 | time_point | exp | |
|---|---|---|---|---|---|---|
| <chr> | <chr> | <lgl> | <lgl> | <fct> | <dbl> | |
| 1 | MYL6 | pos | TRUE | TRUE | time0_exp | -1.2952663 |
| 2 | FN1 | pos | TRUE | TRUE | time0_exp | -0.7126312 |
| 3 | NQO1 | pos | TRUE | TRUE | time0_exp | -0.4633937 |
| 4 | GSTM3 | pos | TRUE | TRUE | time0_exp | -0.4995057 |
| 5 | PRDX1 | pos | TRUE | TRUE | time0_exp | -0.7079641 |
| 6 | LAPTM4A | pos | TRUE | TRUE | time0_exp | -1.1327481 |
In [9]:
p<-ggplot(drivers_exp_long,aes(x=time_point,y=exp,group = V1,color = direction))+
ggalt::geom_xspline(spline_shape = -0.5)+
#geom_line()+
geom_point(size=3)+
theme_bw()+
ggsci::scale_colour_npg()
p
Registered S3 methods overwritten by 'ggalt':
method from
grid.draw.absoluteGrob ggplot2
grobHeight.absoluteGrob ggplot2
grobWidth.absoluteGrob ggplot2
grobX.absoluteGrob ggplot2
grobY.absoluteGrob ggplot2
Warning message:
“Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.”
In [213]:
pos_drivers <- drivers_exp_long %>% filter(direction=='pos')
neg_drivers <- drivers_exp_long %>% filter(direction=='neg')
In [11]:
p2<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
ggalt::geom_xspline(spline_shape = -0.5)+
ggalt::geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5)+
geom_line()+
geom_point(size=3)+
geom_point(data = pos_drivers%>% filter(top20),size=3)+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#D94032FF','FALSE'='#00000033'))
p2
In [29]:
p2<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
ggbump::geom_bump(smooth = 4)+
ggbump::geom_bump(data = pos_drivers%>% filter(top20),smooth = 4)+
#geom_line()+
geom_point(size=3)+
geom_point(data = pos_drivers%>% filter(top20),size=3)+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#D94032FF','FALSE'='#00000033'))
p2
In [32]:
ggsave(plot = p2,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggbump.pdf',width =10, height =5)
ggsave(plot = p2,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggbump.png',width =10, height =5)
In [17]:
#https://github.com/hrbrmstr/ggalt/issues/60
library(ggalt)
GeomXSpline <- ggplot2::ggproto("GeomXSpline", ggplot2::Geom,
required_aes = c("x", "y"),
default_aes = ggplot2::aes(colour = "black",
size = 0.5,
linetype = 1,
alpha = 1,
spline_shape=-1,
open=T),
draw_key = ggplot2::draw_key_smooth, # controls what is drawn in legend
draw_group = function(data, panel_params, coord) {
n <- nrow(data)
if (n <= 2) return(grid::nullGrob())
coords <- coord$transform(data, panel_params)
first_row <- coords[1, , drop = FALSE]
grid::xsplineGrob(
coords$x, coords$y,
shape = coords$spline_shape,
open = coords$open[1],
gp = grid::gpar(col = first_row$colour,
lwd = first_row$size * .pt,
alpha = first_row$alpha,
lty = first_row$linetype)
)
}
)
geom_xspline <- function(mapping = NULL,
data = NULL,
stat = "identity",
position = "identity",
spline_shape = -1,
open = T,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
...) {
layer(
geom = GeomXSpline,
mapping = mapping,
data = data,
stat = stat,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(spline_shape = spline_shape,
open = open,
na.rm = na.rm,
...)
)
}
In [37]:
pos_drivers%>% filter(top20) %>% subset(time_point=='time0_exp')
| V1 | direction | top20 | time_point | exp | |
|---|---|---|---|---|---|
| <chr> | <chr> | <lgl> | <fct> | <dbl> | |
| 1 | MYL6 | pos | TRUE | time0_exp | -1.2952663 |
| 2 | FN1 | pos | TRUE | time0_exp | -0.7126312 |
| 3 | NQO1 | pos | TRUE | time0_exp | -0.4633937 |
| 4 | GSTM3 | pos | TRUE | time0_exp | -0.4995057 |
| 5 | PRDX1 | pos | TRUE | time0_exp | -0.7079641 |
| 6 | LAPTM4A | pos | TRUE | time0_exp | -1.1327481 |
| 7 | MDK | pos | TRUE | time0_exp | -1.1022027 |
| 8 | CD9 | pos | TRUE | time0_exp | -1.2298415 |
| 9 | KRT8 | pos | TRUE | time0_exp | -0.7827570 |
| 10 | CTSA | pos | TRUE | time0_exp | -1.0257106 |
| 11 | HIST1H2BD | pos | TRUE | time0_exp | -0.5420957 |
| 12 | ALPP | pos | TRUE | time0_exp | -0.5967594 |
| 13 | PGK1 | pos | TRUE | time0_exp | -0.4168932 |
In [271]:
#修改 R 中的 ggplot X 轴刻度标签https://www.delftstack.com/zh/howto/r/ggplot-axis-tick-labels-in-r/
#如何修改坐标轴的刻度间隔:https://blog.csdn.net/qq_42458954/article/details/112604443
p2<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
geom_xspline(spline_shape = -0.5)+
geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
geom_point(size=3)+
geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#259CA2BB','FALSE'='#00000033'))+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
#ylim(-1.5,1.5)+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
p2
In [272]:
p3<-ggplot(data = pos_drivers%>% filter(top20),aes(x=time_point,y=exp,group = V1))+
geom_xspline(spline_shape = -0.5,size=1.5,color= '#259CA2AA')+
ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black',color= '#259CA2FF')+
geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5,color= '#259CA2FF')+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
p3
In [273]:
ggsave(plot = p2,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggalt.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = p2,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggalt.png',
width =16/1.5, height =9/1.5)
ggsave(plot = p3,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggalt_only.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = p3,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pos_driver_cross_time_ggalt_only.png',
width =16/1.5, height =9/1.5)
In [275]:
p4<-ggplot(neg_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
geom_xspline(spline_shape = -0.5)+
geom_xspline(data = neg_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
geom_point(size=3)+
geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#E0A965BB','FALSE'='#00000033'))+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
p4
In [276]:
p5<-ggplot(data = neg_drivers%>% filter(top20),aes(x=time_point,y=exp,group = V1))+
geom_xspline(spline_shape = -0.5,size=1.5,color= '#E0A965AA')+
ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black',color= '#E0A965FF')+
geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5,color= '#E0A965FF')+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
p5
In [277]:
ggsave(plot = p4,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_neg_driver_cross_time_ggalt.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = p4,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_neg_driver_cross_time_ggalt.png',
width =16/1.5, height =9/1.5)
ggsave(plot = p5,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_neg_driver_cross_time_ggalt_only.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = p5,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_neg_driver_cross_time_ggalt_only.png',
width =16/1.5, height =9/1.5)
In [440]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“6.35% of input gene IDs are fail to map...”
In [468]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, entrez_gene)
m_t2g$gs_name <- m_t2g$gs_name %>%
stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
stringr::str_to_title()
kk <- enrichKEGG(gene = names(gs),
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
In [485]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()
for (i in 1:nrow(em@result)){
if (i==1){
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [486]:
#ggplot2 修改图例的一些操作 https://zhuanlan.zhihu.com/p/166529941
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.487
In [487]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pRidge_H.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pRidge_H.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.487 Picking joint bandwidth of 0.487
In [488]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()
for (i in 1:nrow(kk@result[1:30,])){
if (i==1){
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [489]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.4
In [490]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pRidge_K.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_pRidge_K.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.4 Picking joint bandwidth of 0.4
In [785]:
increase_control_details0 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/increase_control_details.csv')
head(increase_control_details0)
| V1 | sample_idx | sample_name | score | prob_delta | causal_delta | n_iter |
|---|---|---|---|---|---|---|
| <int> | <int> | <chr> | <dbl> | <dbl> | <dbl> | <int> |
| 0 | 0 | 0-0 | 3.064079e-03 | 3.980619e-02 | 2.45427750 | 199 |
| 1 | 1 | 1-0 | 4.584978e-04 | 5.650461e-03 | 2.32819680 | 199 |
| 2 | 2 | 2-0 | 3.064079e-03 | 3.980619e-02 | 2.45427750 | 199 |
| 3 | 3 | 3-0 | 9.449210e-03 | 1.251141e-01 | 2.50140500 | 199 |
| 4 | 4 | 4-0 | 4.479431e-02 | 7.928820e-01 | 3.34394260 | 199 |
| 5 | 5 | 0-1 | 1.336331e-05 | 4.768372e-07 | 0.01488078 | 11 |
In [786]:
increase_control_details7 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t7_t14_cyc/increase_control_details.csv')
head(increase_control_details7)
| V1 | sample_idx | sample_name | score | prob_delta | causal_delta | n_iter |
|---|---|---|---|---|---|---|
| <int> | <int> | <chr> | <dbl> | <dbl> | <dbl> | <int> |
| 0 | 0 | 0-0 | 0.02251634 | 0.9787240 | 8.211747e+00 | 199 |
| 1 | 1 | 1-0 | 0.02712480 | 0.9868764 | 6.873363e+00 | 199 |
| 2 | 2 | 2-0 | 0.02251634 | 0.9787240 | 8.211747e+00 | 199 |
| 3 | 3 | 3-0 | 0.02285587 | 0.9861129 | 8.150834e+00 | 199 |
| 4 | 4 | 4-0 | 0.02638604 | 0.9834671 | 7.041394e+00 | 199 |
| 5 | 5 | 0-1 | 0.00000000 | 0.0000000 | 1.304077e-07 | 11 |
In [787]:
increase_control_details0$group = 'T0 to T14'
increase_control_details7$group = 'T7 to T14'
In [788]:
increase_control_details <- rbind(increase_control_details0[1:5,],increase_control_details7[1:5,])
In [789]:
increase_control_details
| V1 | sample_idx | sample_name | score | prob_delta | causal_delta | n_iter | group |
|---|---|---|---|---|---|---|---|
| <int> | <int> | <chr> | <dbl> | <dbl> | <dbl> | <int> | <chr> |
| 0 | 0 | 0-0 | 0.0030640793 | 0.039806187 | 2.454277 | 199 | T0 to T14 |
| 1 | 1 | 1-0 | 0.0004584978 | 0.005650461 | 2.328197 | 199 | T0 to T14 |
| 2 | 2 | 2-0 | 0.0030640793 | 0.039806187 | 2.454277 | 199 | T0 to T14 |
| 3 | 3 | 3-0 | 0.0094492097 | 0.125114143 | 2.501405 | 199 | T0 to T14 |
| 4 | 4 | 4-0 | 0.0447943089 | 0.792882010 | 3.343943 | 199 | T0 to T14 |
| 0 | 0 | 0-0 | 0.0225163379 | 0.978723986 | 8.211747 | 199 | T7 to T14 |
| 1 | 1 | 1-0 | 0.0271247989 | 0.986876356 | 6.873363 | 199 | T7 to T14 |
| 2 | 2 | 2-0 | 0.0225163379 | 0.978723986 | 8.211747 | 199 | T7 to T14 |
| 3 | 3 | 3-0 | 0.0228558662 | 0.986112905 | 8.150834 | 199 | T7 to T14 |
| 4 | 4 | 4-0 | 0.0263860431 | 0.983467092 | 7.041394 | 199 | T7 to T14 |
In [790]:
wilcox.test(score~group,data=increase_control_details,alternative = 'less',exact = F)
Wilcoxon rank sum test with continuity correction data: score by group W = 5, p-value = 0.07062 alternative hypothesis: true location shift is less than 0
In [791]:
#Kolmogorov-Smirnov test.
stats::ks.test(score~group,data=increase_control_details,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): “p-value will be approximate in the presence of ties”
Asymptotic two-sample Kolmogorov-Smirnov test data: score by group D^+ = 0.8, p-value = 0.04076 alternative hypothesis: the CDF of T0 to T14 lies above that of T7 to T14
In [792]:
controlp <- ggplot(data = increase_control_details,aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
comparisons = list(c('T0 to T14','T7 to T14')),
annotations= c('P-value = 0.04076'),textsize = 8,size=1.5,vjust=0) +
theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T0 to T14'= '#559073FF','T7 to T14'='#D28130FF'))+
scale_color_manual(values = c('T0 to T14'= '#559073FF','T7 to T14'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top')
controlp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [793]:
ggsave(plot = controlp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_controlp.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = controlp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_controlp.png',
width =16/1.5, height =16/1.5)
Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.”
In [775]:
decrease_control_details0 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/decrease_control_details.csv')
tail(decrease_control_details0)
decrease_control_details7 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t7_t14_cyc/decrease_control_details.csv')
tail(decrease_control_details7)
decrease_control_details0$group = 'T14 to T0'
decrease_control_details7$group = 'T14 to T7'
decrease_control_details <- rbind(decrease_control_details0[6:13,],decrease_control_details7[6:13,])
| V1 | sample_idx | sample_name | score | prob_delta | causal_delta | n_iter |
|---|---|---|---|---|---|---|
| <int> | <int> | <chr> | <dbl> | <dbl> | <dbl> | <int> |
| 7 | 7 | 2-1 | 0.0205232458 | 0.98098514 | 9.030036 | 199 |
| 8 | 8 | 3-1 | 0.0140026616 | 0.97204053 | 13.114353 | 199 |
| 9 | 9 | 4-1 | 0.0001605263 | 0.00163569 | 1.924987 | 199 |
| 10 | 10 | 5-1 | 0.0161905877 | 0.97274728 | 11.350383 | 199 |
| 11 | 11 | 6-1 | 0.0160666327 | 0.96472133 | 11.343579 | 199 |
| 12 | 12 | 7-1 | 0.0146326922 | 0.95631039 | 12.346610 | 199 |
| V1 | sample_idx | sample_name | score | prob_delta | causal_delta | n_iter |
|---|---|---|---|---|---|---|
| <int> | <int> | <chr> | <dbl> | <dbl> | <dbl> | <int> |
| 7 | 7 | 2-1 | NA | 0.000000e+00 | 0.0000000 | 11 |
| 8 | 8 | 3-1 | 8.516641e-08 | 1.192093e-07 | 0.5837294 | 11 |
| 9 | 9 | 4-1 | NA | 0.000000e+00 | 0.0000000 | 11 |
| 10 | 10 | 5-1 | 8.061132e-08 | 1.192093e-07 | 0.6167141 | 11 |
| 11 | 11 | 6-1 | 1.311363e-02 | 9.920255e-01 | 14.2913460 | 199 |
| 12 | 12 | 7-1 | 3.620758e-07 | 8.344650e-07 | 0.8985244 | 13 |
In [777]:
#Kolmogorov-Smirnov test.
stats::ks.test(score~group,data=decrease_control_details,alternative = 'less',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): “p-value will be approximate in the presence of ties”
Asymptotic two-sample Kolmogorov-Smirnov test data: score by group D^- = 0.875, p-value = 0.005248 alternative hypothesis: the CDF of T14 to T0 lies below that of T14 to T7
In [783]:
Dcontrolp <- ggplot(data = decrease_control_details,aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
comparisons = list(c('T14 to T0','T14 to T7')),
annotations= c('P-value = 0.005248'),textsize = 8,size=1.5,vjust=0) +
theme_classic()+
xlab('Time point modeling')+
ylab('Status decreasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T14 to T0'= '#559073FF','T14 to T7'='#D28130FF'))+
scale_color_manual(values = c('T14 to T0'= '#559073FF','T14 to T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top')
Dcontrolp
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Warning message:
“Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
Warning message:
“Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).”
In [784]:
ggsave(plot = Dcontrolp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_Dcontrolp.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = Dcontrolp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_Dcontrolp.png',
width =16/1.5, height =16/1.5)
Warning message: “Removed 2 rows containing non-finite outside the scale range (`stat_boxplot()`).” Warning message: “Removed 2 rows containing non-finite outside the scale range (`stat_boxplot()`).” Warning message: “Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Warning message: “Removed 2 rows containing missing values or values outside the scale range (`geom_point()`).” Warning message: “Removed 2 rows containing non-finite outside the scale range (`stat_boxplot()`).” Warning message: “Removed 2 rows containing non-finite outside the scale range (`stat_boxplot()`).” Warning message: “Removed 2 rows containing non-finite outside the scale range (`stat_signif()`).” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Warning message: “Removed 2 rows containing missing values or values outside the scale range (`geom_point()`).”
In [906]:
increase_control_details_t7Driver<- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_adata_increase_t7Driver.csv')
In [907]:
increase_control_details_t7Driver$group = c(rep('T0',times = 5),rep('T14',times = 8),rep('T7',times = 5))
In [913]:
#Kolmogorov-Smirnov test.
tmp1 <- increase_control_details_t7Driver[c(1:5,14:18),]
stats::ks.test(score~group,data=tmp1,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): “p-value will be approximate in the presence of ties”
Asymptotic two-sample Kolmogorov-Smirnov test data: score by group D^+ = 0.8, p-value = 0.04076 alternative hypothesis: the CDF of T0 lies above that of T7
In [916]:
controlp <- ggplot(data = increase_control_details_t7Driver[c(1:5,14:18),],aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
comparisons = list(c('T0','T7')),
annotations= c('P-value = 0.04076'),textsize = 8,size=1.5,vjust=0) +
theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
scale_color_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top')
controlp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [917]:
increase_control_details_t0Driver<- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_adata_increase_t0Driver.csv')
In [921]:
increase_control_details_t0Driver$group = c(rep('T0',times = 5),rep('T14',times = 8),rep('T7',times = 5))
In [922]:
#Kolmogorov-Smirnov test.
tmp1 <- increase_control_details_t0Driver[c(1:5,14:18),]
stats::ks.test(score~group,data=tmp1,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): “p-value will be approximate in the presence of ties”
Asymptotic two-sample Kolmogorov-Smirnov test data: score by group D^+ = 0.8, p-value = 0.04076 alternative hypothesis: the CDF of T0 lies above that of T7
In [923]:
controlp <- ggplot(data = increase_control_details_t7Driver[c(1:5,14:18),],aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
comparisons = list(c('T0','T7')),
annotations= c('P-value = 0.04076'),textsize = 8,size=1.5,vjust=0) +
theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
scale_color_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top')
controlp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [937]:
tmp <- rbind(increase_control_details_t0Driver[c(1:5),],increase_control_details_t7Driver[c(14:18),])
#Kolmogorov-Smirnov test.
stats::ks.test(score~group,data=tmp,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): “p-value will be approximate in the presence of ties”
Asymptotic two-sample Kolmogorov-Smirnov test data: score by group D^+ = 0.8, p-value = 0.04076 alternative hypothesis: the CDF of T0 lies above that of T7
In [938]:
controlp <- ggplot(data = tmp,aes(x = group,y = score,fill= group))+
stat_boxplot(geom = "errorbar",linewidth=1.5)+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
comparisons = list(c('T0','T7')),
annotations= c('P-value = 0.04076'),textsize = 8,size=1.5,vjust=0) +
theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
scale_color_manual(values = c('T0'= '#559073FF','T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top')
controlp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [939]:
ggsave(plot = controlp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_7_controlp.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = controlp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_7_controlp.png',
width =16/1.5, height =16/1.5)
Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.”
In [147]:
decrease_control_details_t7Driver<- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_adata_decrease_t7Driver.csv')
decrease_control_details_t0Driver<- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_adata_decrease_t0Driver.csv')
In [941]:
decrease_control_details_t7Driver$group = c(rep('T0',times = 5),rep('T14 T0',times = 8),rep('T7',times = 5))
decrease_control_details_t0Driver$group = c(rep('T0',times = 5),rep('T14 T7',times = 8),rep('T7',times = 5))
In [942]:
#Kolmogorov-Smirnov test.
tmp1 <- decrease_control_details_t7Driver[c(6:13),]
tmp2 <- decrease_control_details_t0Driver[c(6:13),]
tmp <- rbind(tmp1,tmp2)
stats::ks.test(score~group,data=tmp,alternative = 'greater',exact = F)
Warning message in ks.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): “p-value will be approximate in the presence of ties”
Asymptotic two-sample Kolmogorov-Smirnov test data: score by group D^+ = 0.625, p-value = 0.04394 alternative hypothesis: the CDF of T14 T0 lies above that of T14 T7
In [943]:
Dcontrolp <- ggplot(data = tmp,aes(x = group,y = score,fill= group))+
#stat_boxplot(geom = "errorbar",linewidth=1.5)+
#geom_violin(outliers = F,linewidth=1.5,color='black')+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
comparisons = list(c('T14 T0','T14 T7')),
annotations= c('P-value = 0.04394'),textsize = 8,size=1.5,vjust=0) +
theme_classic()+
xlab('Time point modeling')+
ylab('Status increasing control score')+
labs(fill="Genes Type")+
theme_bw()+
scale_fill_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
scale_color_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top')
Dcontrolp
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [944]:
ggsave(plot = Dcontrolp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_7_Dcontrolp.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = Dcontrolp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_7_Dcontrolp.png',
width =16/1.5, height =16/1.5)
Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.”
In [ ]:
In [170]:
diff_gene_exp_meatcell4 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/diff_gene_exp_meatcell4.csv')
In [171]:
head(diff_gene_exp_meatcell4)
| V1 | Gene | FDR | pvals | logFC |
|---|---|---|---|---|
| <int> | <chr> | <dbl> | <dbl> | <dbl> |
| 0 | HIST1H1D | 4.939636e-19 | 2.228474e-23 | 6.8936324 |
| 1 | HIST1H4C | 5.657670e-04 | 5.104818e-08 | 1.2869049 |
| 2 | YBX1 | 2.927308e-02 | 3.961889e-06 | 0.3632599 |
| 3 | MALAT1 | 1.794645e-01 | 3.238555e-05 | 0.7508955 |
| 4 | CTNNAL1 | 1.000000e+00 | 8.934546e-04 | 0.7036244 |
| 5 | NFE2L3 | 1.000000e+00 | 1.184413e-03 | 1.0585847 |
In [172]:
diff_gene_exp_meatcell4 <- diff_gene_exp_meatcell4 %>%mutate(change = as.factor(ifelse(pvals < 0.05 & abs(logFC) > 1.2,
ifelse(logFC > 1 ,'Up','Down'),'No change')))
In [155]:
dim(diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',])
- 10
- 6
In [156]:
diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',]
| V1 | Gene | FDR | pvals | logFC | change |
|---|---|---|---|---|---|
| <int> | <chr> | <dbl> | <dbl> | <dbl> | <fct> |
| 22133 | ALKBH7 | 1 | 0.039087293 | -1.752925 | Down |
| 22139 | P2RY6 | 1 | 0.031032151 | -27.488358 | Down |
| 22144 | KRTCAP2 | 1 | 0.026946023 | -1.358575 | Down |
| 22146 | ACO2 | 1 | 0.022090860 | -2.060025 | Down |
| 22148 | AIP | 1 | 0.020344494 | -2.571658 | Down |
| 22150 | CHURC1 | 1 | 0.018870283 | -1.314827 | Down |
| 22151 | PSAP | 1 | 0.016376696 | -1.446578 | Down |
| 22154 | NELFCD | 1 | 0.014387572 | -1.708666 | Down |
| 22158 | SVBP | 1 | 0.011970509 | -1.389756 | Down |
| 22161 | C6orf1 | 1 | 0.008870535 | -1.309893 | Down |
In [157]:
dim(diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Up',])
- 18
- 6
In [158]:
diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Up',]
| V1 | Gene | FDR | pvals | logFC | change |
|---|---|---|---|---|---|
| <int> | <chr> | <dbl> | <dbl> | <dbl> | <fct> |
| 0 | HIST1H1D | 4.939636e-19 | 2.228474e-23 | 6.893632 | Up |
| 1 | HIST1H4C | 5.657670e-04 | 5.104818e-08 | 1.286905 | Up |
| 6 | SUZ12 | 1.000000e+00 | 1.600379e-03 | 1.526876 | Up |
| 14 | COX19 | 1.000000e+00 | 8.342897e-03 | 1.276946 | Up |
| 15 | DAG1 | 1.000000e+00 | 8.948297e-03 | 1.372644 | Up |
| 19 | VSTM1 | 1.000000e+00 | 1.291202e-02 | 1.842837 | Up |
| 20 | BRCA1 | 1.000000e+00 | 1.300847e-02 | 1.459212 | Up |
| 26 | ZFP91 | 1.000000e+00 | 1.543801e-02 | 1.565769 | Up |
| 27 | CCND3 | 1.000000e+00 | 1.598086e-02 | 1.232958 | Up |
| 29 | POLR3A | 1.000000e+00 | 1.621440e-02 | 2.377943 | Up |
| 30 | MECOM | 1.000000e+00 | 1.801522e-02 | 1.957915 | Up |
| 33 | CENPB | 1.000000e+00 | 1.882000e-02 | 1.725816 | Up |
| 45 | CNOT11 | 1.000000e+00 | 2.752299e-02 | 1.203864 | Up |
| 53 | MAVS | 1.000000e+00 | 3.008922e-02 | 1.467143 | Up |
| 55 | LAS1L | 1.000000e+00 | 3.123933e-02 | 1.597762 | Up |
| 57 | VEZT | 1.000000e+00 | 3.242696e-02 | 1.305980 | Up |
| 64 | HKDC1 | 1.000000e+00 | 3.666902e-02 | 1.786746 | Up |
| 80 | ZCCHC10 | 1.000000e+00 | 4.724312e-02 | 1.230941 | Up |
In [159]:
diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$Gene == 'P2RY6','logFC'] = diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$Gene == 'P2RY6','logFC']+20
In [763]:
volcanop<- ggplot(diff_gene_exp_meatcell4 %>% filter(abs(logFC)<10),aes(logFC, -log10(pvals)))+
geom_hline(yintercept = -log10(0.05), linetype = 5, color = "black",linewidth = 1)+
geom_vline(xintercept = c(-1.2,1.2), linetype = 5, color = "black",linewidth = 1)+
geom_point(aes(color = change),
size = 4,
alpha = 0.75) +
ggrepel::geom_text_repel(data =diff_gene_exp_meatcell4 %>% filter(abs(logFC)<10) %>% filter(change %in% c('Up','Down')),
max.overlaps = getOption("ggrepel.max.overlaps", default = 30),
# 这里的filter很关键,筛选你想要标记的基因
aes(label = Gene),
size = 5, box.padding = unit(2.5, "mm"),
color = 'black') +
theme_bw(base_size = 12)+
scale_color_manual(values = c('Up'='#0FB9F3','Down'='#EA5A18','No change'='#BBBBBB')) +
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')+
xlab("Log2FC")+
ylab("-Log10(P-value)")
volcanop
Warning message: “ggrepel: 15 unlabeled data points (too many overlaps). Consider increasing max.overlaps”
In [764]:
ggsave(plot = volcanop,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_volcanop.pdf',
width =14/1.5, height =16/1.5)
ggsave(plot = volcanop,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_volcanop.png',
width =14/1.5, height =16/1.5)
In [772]:
diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',]$Gene %in% drivers$V1
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
In [174]:
up_0 <- diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Up',]$Gene
down_0 <- diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',]$Gene
In [175]:
diff_gene_exp_meatcell4 <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/main_PC9_LUNG_run10_t0_t14_cyc/diff_gene_exp14_meatcell4.csv')
In [176]:
diff_gene_exp_meatcell4 <- diff_gene_exp_meatcell4 %>%mutate(change = as.factor(ifelse(pvals < 0.05 & abs(logFC) > 1.2,
ifelse(logFC > 1 ,'Up','Down'),'No change')))
In [183]:
volcanop<- ggplot(diff_gene_exp_meatcell4 %>% filter(abs(logFC)<10),aes(logFC, -log10(pvals)))+
geom_hline(yintercept = -log10(0.05), linetype = 5, color = "black",linewidth = 1)+
geom_vline(xintercept = c(-1.2,1.2), linetype = 5, color = "black",linewidth = 1)+
geom_point(aes(color = change),
size = 4,
alpha = 0.75) +
ggrepel::geom_text_repel(data =diff_gene_exp_meatcell4 %>%
filter(abs(logFC)<10) %>%
filter(change %in% c('Up','Down')) %>%
filter(-log10(pvals)>8 | abs(logFC) > 3),
max.overlaps = getOption("ggrepel.max.overlaps", default = 30),
# 这里的filter很关键,筛选你想要标记的基因
aes(label = Gene),
size = 5, box.padding = unit(2.5, "mm"),
color = 'black') +
theme_bw(base_size = 12)+
scale_color_manual(values = c('Up'='#0FB9F3','Down'='#EA5A18','No change'='#BBBBBB')) +
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')+
xlab("Log2FC")+
ylab("-Log10(P-value)")
volcanop
In [184]:
ggsave(plot = volcanop,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_volcanop_14low.pdf',
width =14/1.5, height =16/1.5)
ggsave(plot = volcanop,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_14_volcanop_14low.png',
width =14/1.5, height =16/1.5)
In [177]:
up_14 <- diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Up',]$Gene
down_14 <- diff_gene_exp_meatcell4[diff_gene_exp_meatcell4$change == 'Down',]$Gene
In [178]:
a <- list(up_0 = up_0,
down_0 = down_0,
up_14 = up_14,
down_14 = down_14)
p1=ggvenn(a, c("up_0", "down_0","up_14",'down_14'))
p1
t7_14¶
In [514]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t7_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [515]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| BIN1 | 0.003354605 | 0.0003882261 | 0.0005153501 | 10 | FALSE | FALSE | FALSE | FALSE |
| CKS1B | 0.002709994 | 0.0005753519 | 0.0004540569 | 10 | FALSE | FALSE | FALSE | FALSE |
| PRDX1 | 0.003196059 | 0.0005124655 | 0.0004087128 | 10 | FALSE | FALSE | TRUE | TRUE |
| KRT8 | 0.003889918 | 0.0002229171 | 0.0005481308 | 10 | FALSE | FALSE | FALSE | FALSE |
| SRSF3 | 0.002838213 | 0.0003422896 | 0.0004343811 | 10 | FALSE | FALSE | FALSE | FALSE |
| TPM1 | 0.002373309 | 0.0003037851 | -0.0005296845 | 10 | FALSE | FALSE | FALSE | FALSE |
In [516]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [517]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| PRDX1 | 0.003196059 | 0.0005124655 | 0.0004087128 | 10 | FALSE | FALSE | TRUE | TRUE | 13 | 22 |
| H2AFZ | 0.004075658 | 0.0005847643 | 0.0006475787 | 10 | FALSE | TRUE | FALSE | TRUE | 8 | 8 |
| ALDH3A1 | 0.004452039 | 0.0005462090 | 0.0007624793 | 10 | FALSE | TRUE | FALSE | TRUE | 6 | 5 |
| NQO1 | 0.002075033 | 0.0002167071 | 0.0003687808 | 9 | FALSE | FALSE | TRUE | TRUE | 23 | 24 |
In [495]:
fread(file.path(read_dir,'all_driver_types_match_counts.csv'))
| seed | shap_total | shap_0 | shap_1 | grad_total | grad_0 | grad_1 |
|---|---|---|---|---|---|---|
| <int> | <int> | <int> | <int> | <int> | <int> | <int> |
| 0 | 27 | 27 | 26 | 23 | 5 | 22 |
| 1 | 25 | 25 | 25 | 25 | 21 | 25 |
| 2 | 26 | 26 | 25 | 24 | 23 | 19 |
| 3 | 27 | 28 | 27 | 25 | 24 | 25 |
| 4 | 27 | 26 | 28 | 26 | 22 | 25 |
| 5 | 28 | 28 | 27 | 24 | 24 | 25 |
| 6 | 27 | 27 | 28 | 26 | 27 | 25 |
| 7 | 26 | 26 | 26 | 26 | 21 | 24 |
| 8 | 27 | 26 | 25 | 24 | 24 | 22 |
| 9 | 26 | 26 | 26 | 24 | 23 | 17 |
In [497]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [498]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [499]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [500]:
pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
In [501]:
plot_df$pw = plot_df$Gene %in% pathway_hallmark$HALLMARK_FATTY_ACID_METABOLISM
In [137]:
ggplot(data = plot_df)+
geom_boxplot(aes(x=variable,y=value,fill=pw))+
theme_classic()
Warning message: “Removed 398 rows containing non-finite outside the scale range (`stat_boxplot()`).”
In [502]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [503]:
tmp <- plot_df[plot_df$Gene %in% gl,]
tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [504]:
gl
- 'GSTM3'
- 'UBB'
- 'ITGA2'
- 'PKM'
- 'MYL6'
- 'ALDH3A1'
- 'ENO1'
- 'H2AFZ'
- 'HSPA1A'
- 'KRT8'
In [141]:
ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
theme(legend.position = "none")
Picking joint bandwidth of 0.000269
In [518]:
drivers$norm_shap <- normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = rev(ddf$V1))
In [519]:
bar_p <- ggplot(ddf) +
geom_bar(aes(x = V1, y = norm_shap,fill=norm_shap),stat = "identity",width = 0.6, alpha=1)+
scale_fill_gradient(low = "#659999",high = "#f4791f")+
coord_flip() +
theme_bw()+
ylab('Normalized SHAP weight')+
xlab('Driver genes')+
ggtitle("Modeling between T7 T14")+
#scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
axis.text.y = element_text(colour = c(rep('black',times = 2),'#BA002B','black','#BA002B',rep('black',times = 5))),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(size = 1.5),
panel.grid = element_blank(),
panel.grid.minor.x = element_line(linetype = 5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
legend.position = 'none'
)
bar_p
Warning message:
“Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.”
In [509]:
ggsave(plot = bar_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_bar_p.pdf',
width =12/1.5, height =16/1.5)
ggsave(plot = bar_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_bar_p.png',
width =12/1.5, height =16/1.5)
In [520]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [521]:
drivers_exp_long<-drivers_exp %>%as.data.frame %>%
mutate(direction=ifelse(weight_grad_total_dir_mean > 0,'pos','neg')) %>%
mutate(rank=rank(-drivers_exp$weight_shap_total_mean)) %>%
mutate(top20 = ifelse(rank<=20,TRUE,FALSE)) %>%
mutate(top10 = ifelse(rank<=10,TRUE,FALSE)) %>%
dplyr::select(c('V1','time0_exp','time3_exp','time7_exp','time14_exp','direction','top20','top10')) %>%
reshape2::melt(id.vars = c("V1",'direction','top20','top10'), #需保留的不参与聚合的变量列名
measure.vars = c('time0_exp','time3_exp','time7_exp','time14_exp'),#需要聚合的变量s1-s10
variable.name = c('time_point'),#聚合变量的新列名
value.name = 'exp')#聚合值的新列名
In [522]:
head(drivers_exp_long)
| V1 | direction | top20 | top10 | time_point | exp | |
|---|---|---|---|---|---|---|
| <chr> | <chr> | <lgl> | <lgl> | <fct> | <dbl> | |
| 1 | GSTM3 | pos | TRUE | TRUE | time0_exp | -0.4995057 |
| 2 | UBB | pos | TRUE | TRUE | time0_exp | -0.4454428 |
| 3 | ITGA2 | neg | TRUE | TRUE | time0_exp | 0.1798690 |
| 4 | PKM | pos | TRUE | TRUE | time0_exp | -0.1132269 |
| 5 | MYL6 | pos | TRUE | TRUE | time0_exp | -1.2952663 |
| 6 | ALDH3A1 | pos | TRUE | TRUE | time0_exp | -0.5168157 |
In [523]:
pos_drivers <- drivers_exp_long %>% filter(direction=='pos')
neg_drivers <- drivers_exp_long %>% filter(direction=='neg')
In [550]:
pos_time_p<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
geom_xspline(spline_shape = -0.5)+
geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
geom_point(size=3)+
geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#259CA2BB','FALSE'='#00000033'))+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
#ylim(-1.5,1.5)+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
pos_time_p
Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_x_spline()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_point()`).”
In [551]:
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pos_driver_cross_time_ggalt.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pos_driver_cross_time_ggalt.png',
width =16/1.5, height =9/1.5)
Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_x_spline()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_point()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_x_spline()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_point()`).”
In [552]:
neg_time_p<-ggplot(neg_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
geom_xspline(spline_shape = -0.5)+
geom_xspline(data = neg_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
geom_point(size=3)+
geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#E0A965BB','FALSE'='#00000033'))+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
neg_time_p
In [553]:
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_neg_driver_cross_time_ggalt.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_neg_driver_cross_time_ggalt.png',
width =16/1.5, height =9/1.5)
In [536]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“1.67% of input gene IDs are fail to map...”
In [541]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, entrez_gene)
m_t2g$gs_name <- m_t2g$gs_name %>%
stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
stringr::str_to_title()
kk <- enrichKEGG(gene = names(gs),
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
In [542]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()
for (i in 1:nrow(em@result)){
if (i==1){
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [543]:
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = ' ') +
scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.text.y = element_text(size = 10,color="black"),
axis.text.x = element_text(size = 12,color="black"),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
)
pRidge
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.403
In [544]:
#ggplot2 修改图例的一些操作 https://zhuanlan.zhihu.com/p/166529941
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.403
In [545]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pRidge_H.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pRidge_H.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.403 Picking joint bandwidth of 0.403
In [546]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()
for (i in 1:nrow(kk@result[1:30,])){
if (i==1){
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [547]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'KEGG',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.265
In [548]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pRidge_K.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t7_14_pRidge_K.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.265 Picking joint bandwidth of 0.265
In [8]:
chord_data <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_t7_orgin_state_transition_adata_meta.csv')
In [9]:
chord_data <- chord_data %>% select(c('condition','metacell_label2'))
In [10]:
# Transform input data in a adjacency matrix
adjacencyData <- with(chord_data, table(condition, metacell_label2))
In [11]:
adjacencyData
metacell_label2
condition 0 in 0 0 in 0-7 0 in 1 1 in 0 1 in 0-7 1 in 1 2 in 0 2 in 0-7
t0 cycling 537 0 0 110 0 0 106 0
t14 cycling 0 0 762 0 0 346 0 0
t7 cycling 0 281 0 0 223 0 0 191
metacell_label2
condition 2 in 1 3 in 0 3 in 0-7 3 in 1 4 in 0 4 in 0-7 4 in 1 5 in 1
t0 cycling 0 88 0 0 37 0 0 0
t14 cycling 237 0 0 176 0 0 159 133
t7 cycling 0 0 87 0 0 36 0 0
metacell_label2
condition 6 in 1 7 in 1 8 in 1
t0 cycling 0 0 0
t14 cycling 120 51 9
t7 cycling 0 0 0
In [49]:
dim(adjacencyData)
- 3
- 19
In [53]:
#https://www.jianshu.com/p/1ebeaaed6f4e
library(RColorBrewer)
display.brewer.all() #显示所有调色板
In [56]:
grid.col <-c( brewer.pal(8, "Set2"), brewer.pal(8, "Accent"),brewer.pal(6, "Set3"))
grid.col
- '#66C2A5'
- '#FC8D62'
- '#8DA0CB'
- '#E78AC3'
- '#A6D854'
- '#FFD92F'
- '#E5C494'
- '#B3B3B3'
- '#7FC97F'
- '#BEAED4'
- '#FDC086'
- '#FFFF99'
- '#386CB0'
- '#F0027F'
- '#BF5B17'
- '#666666'
- '#8DD3C7'
- '#FFFFB3'
- '#BEBADA'
- '#FB8072'
- '#80B1D3'
- '#FDB462'
In [ ]:
In [57]:
#https://www.jianshu.com/p/73c246b87d82
chordDiagram(
adjacencyData, grid.col = grid.col,
annotationTrack = c("grid"),
preAllocateTracks = list(
track.height = max(strwidth(unlist(dimnames(adjacencyData))))
)
)
circos.track(
track.index = 1, panel.fun = function(x, y) {
circos.text(
CELL_META$xcenter, CELL_META$ylim[1],
CELL_META$sector.index, facing = "clockwise",
niceFacing = TRUE, adj = c(0, 0.5)
)
}, bg.border = NA
)
In [61]:
pdf('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_t7_orgin_state_transition_adata_meta.pdf')
chordDiagram(
adjacencyData, grid.col = grid.col,
annotationTrack = c("grid"),
preAllocateTracks = list(
track.height = max(strwidth(unlist(dimnames(adjacencyData))))
)
)
circos.track(
track.index = 1, panel.fun = function(x, y) {
circos.text(
CELL_META$xcenter, CELL_META$ylim[1],
CELL_META$sector.index, facing = "clockwise",
niceFacing = TRUE, adj = c(0, 0.5)
)
}, bg.border = NA
)
dev.off()
png: 2
t14 cyc_ncyc¶
In [804]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t14_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [795]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| HLA-B | 0.006611837 | 0.0014072035 | -0.003888358 | 10 | FALSE | FALSE | FALSE | FALSE |
| H2AFZ | 0.001543479 | 0.0003817574 | 0.001596191 | 10 | FALSE | TRUE | FALSE | TRUE |
| UBC | 0.003014871 | 0.0006034041 | -0.003552045 | 10 | FALSE | FALSE | FALSE | FALSE |
| IGFBP5 | 0.011728111 | 0.0037999116 | 0.013004079 | 10 | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.009201345 | 0.0016104131 | 0.004694919 | 10 | FALSE | FALSE | FALSE | FALSE |
| SAT1 | 0.003824356 | 0.0007941552 | -0.001987261 | 10 | FALSE | FALSE | FALSE | FALSE |
In [796]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [797]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| H2AFZ | 0.0015434791 | 0.0003817574 | 0.0015961908 | 10 | FALSE | TRUE | FALSE | TRUE | 22 | 20 |
| JUNB | 0.0016835371 | 0.0008096141 | -0.0029814138 | 7 | TRUE | FALSE | TRUE | TRUE | 20 | 9 |
| SOD2 | 0.0002215576 | 0.0000000000 | 0.0007589676 | 1 | FALSE | FALSE | TRUE | TRUE | 63 | 39 |
| LGALS1 | 0.0001387697 | 0.0000000000 | 0.0001830989 | 1 | FALSE | TRUE | FALSE | TRUE | 72 | 65 |
| PFKP | 0.0002421256 | 0.0000000000 | 0.0002091714 | 1 | FALSE | FALSE | TRUE | TRUE | 61 | 63 |
In [798]:
drivers[order(weight_shap_total_mean,decreasing = T),]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| IGFBP5 | 0.0117281109 | 0.0037999116 | 0.0130040787 | 10 | FALSE | FALSE | FALSE | FALSE | 1 | 1 |
| GSTM3 | 0.0092013446 | 0.0016104131 | 0.0046949189 | 10 | FALSE | FALSE | FALSE | FALSE | 2 | 4 |
| HLA-B | 0.0066118369 | 0.0014072035 | -0.0038883580 | 10 | FALSE | FALSE | FALSE | FALSE | 3 | 5 |
| CD24 | 0.0044367598 | 0.0006784051 | 0.0058391402 | 10 | FALSE | FALSE | FALSE | FALSE | 4 | 2 |
| HIST1H4C | 0.0040689346 | 0.0005085617 | -0.0057280286 | 10 | FALSE | FALSE | FALSE | FALSE | 5 | 3 |
| SAT1 | 0.0038243564 | 0.0007941552 | -0.0019872606 | 10 | FALSE | FALSE | FALSE | FALSE | 6 | 16 |
| ELF3 | 0.0031762201 | 0.0007735006 | -0.0031860740 | 9 | TRUE | FALSE | FALSE | FALSE | 7 | 7 |
| UCHL1 | 0.0031085463 | 0.0010805764 | 0.0016903948 | 10 | FALSE | FALSE | FALSE | FALSE | 8 | 19 |
| UBC | 0.0030148711 | 0.0006034041 | -0.0035520454 | 10 | FALSE | FALSE | FALSE | FALSE | 9 | 6 |
| AKR1C3 | 0.0030099362 | 0.0013239239 | 0.0011039679 | 10 | FALSE | FALSE | FALSE | FALSE | 10 | 30 |
| UBE2C | 0.0025699677 | 0.0006142277 | -0.0025862740 | 9 | FALSE | FALSE | FALSE | FALSE | 11 | 12 |
| HLA-C | 0.0025660423 | 0.0005627148 | -0.0007788526 | 9 | FALSE | FALSE | FALSE | FALSE | 12 | 38 |
| IL32 | 0.0022163223 | 0.0002488609 | -0.0001668038 | 5 | FALSE | FALSE | FALSE | FALSE | 13 | 66 |
| CCND1 | 0.0021071244 | 0.0003571276 | -0.0020192598 | 10 | FALSE | FALSE | FALSE | FALSE | 14 | 14 |
| HLA-A | 0.0020687648 | 0.0004496342 | -0.0028002224 | 7 | FALSE | FALSE | FALSE | FALSE | 15 | 10 |
| MT2A | 0.0020467366 | 0.0004843694 | -0.0020090360 | 9 | FALSE | FALSE | FALSE | FALSE | 16 | 15 |
| EIF5A | 0.0018783751 | 0.0004235593 | 0.0013126781 | 9 | FALSE | FALSE | FALSE | FALSE | 17 | 25 |
| SERPINH1 | 0.0017599936 | 0.0005120919 | 0.0027856450 | 8 | FALSE | FALSE | FALSE | FALSE | 18 | 11 |
| CSTB | 0.0017033408 | 0.0003514902 | 0.0025668851 | 9 | FALSE | FALSE | FALSE | FALSE | 19 | 13 |
| JUNB | 0.0016835371 | 0.0008096141 | -0.0029814138 | 7 | TRUE | FALSE | TRUE | TRUE | 20 | 9 |
| LDHB | 0.0015744976 | 0.0009098976 | 0.0003617867 | 8 | FALSE | FALSE | FALSE | FALSE | 21 | 56 |
| H2AFZ | 0.0015434791 | 0.0003817574 | 0.0015961908 | 10 | FALSE | TRUE | FALSE | TRUE | 22 | 20 |
| CTSD | 0.0014267733 | 0.0002987906 | 0.0013005452 | 6 | FALSE | FALSE | FALSE | FALSE | 23 | 26 |
| PTTG1 | 0.0013644307 | 0.0005340962 | 0.0008319057 | 5 | FALSE | FALSE | FALSE | FALSE | 24 | 37 |
| H2AFX | 0.0013287316 | 0.0002748743 | -0.0019340660 | 8 | FALSE | FALSE | FALSE | FALSE | 25 | 17 |
| KLF4 | 0.0013044442 | 0.0005156657 | -0.0031121433 | 8 | TRUE | FALSE | FALSE | FALSE | 26 | 8 |
| RRBP1 | 0.0011925060 | 0.0002110271 | -0.0006431049 | 5 | FALSE | FALSE | FALSE | FALSE | 27 | 44 |
| MYBL2 | 0.0011483909 | 0.0005146034 | 0.0005562596 | 7 | TRUE | FALSE | FALSE | FALSE | 28 | 45 |
| ENO1 | 0.0010815923 | 0.0003481806 | 0.0011467627 | 7 | FALSE | FALSE | FALSE | FALSE | 29 | 29 |
| PGK1 | 0.0009510034 | 0.0002593502 | 0.0015275956 | 5 | FALSE | FALSE | FALSE | FALSE | 30 | 21 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| CCNB1 | 3.756681e-04 | 7.193587e-05 | -5.032483e-04 | 3 | FALSE | FALSE | FALSE | FALSE | 52 | 47 |
| HMGA1 | 3.704270e-04 | 1.749644e-05 | 1.017245e-03 | 2 | TRUE | FALSE | FALSE | FALSE | 53 | 32 |
| TPM1 | 3.272433e-04 | 1.606393e-04 | -4.370322e-04 | 2 | FALSE | FALSE | FALSE | FALSE | 54 | 51 |
| JUND | 3.153070e-04 | 3.122225e-04 | 8.608124e-04 | 3 | TRUE | FALSE | FALSE | FALSE | 55 | 35 |
| CLTB | 3.133257e-04 | 0.000000e+00 | 3.675563e-05 | 1 | FALSE | FALSE | FALSE | FALSE | 56 | 74 |
| PLK2 | 2.963634e-04 | 0.000000e+00 | 1.643981e-04 | 1 | FALSE | FALSE | FALSE | FALSE | 57 | 67 |
| ASF1B | 2.921398e-04 | 1.729619e-05 | 4.507829e-04 | 2 | FALSE | FALSE | FALSE | FALSE | 58 | 49 |
| PCNA | 2.809295e-04 | 1.516539e-04 | 6.670484e-04 | 2 | FALSE | FALSE | FALSE | FALSE | 59 | 42 |
| BIN1 | 2.658711e-04 | 0.000000e+00 | 1.353187e-05 | 1 | FALSE | FALSE | FALSE | FALSE | 60 | 79 |
| PFKP | 2.421256e-04 | 0.000000e+00 | 2.091714e-04 | 1 | FALSE | FALSE | TRUE | TRUE | 61 | 63 |
| CEBPD | 2.357836e-04 | 5.450518e-05 | -7.560967e-04 | 2 | TRUE | FALSE | FALSE | FALSE | 62 | 40 |
| SOD2 | 2.215576e-04 | 0.000000e+00 | 7.589676e-04 | 1 | FALSE | FALSE | TRUE | TRUE | 63 | 39 |
| HSPA5 | 2.087326e-04 | 0.000000e+00 | -4.155351e-04 | 1 | FALSE | FALSE | FALSE | FALSE | 64 | 53 |
| TUBA1B | 1.948145e-04 | 0.000000e+00 | -7.872681e-05 | 1 | FALSE | FALSE | FALSE | FALSE | 65 | 71 |
| CEBPB | 1.856055e-04 | 1.775317e-04 | 6.901383e-05 | 2 | TRUE | FALSE | FALSE | FALSE | 66 | 72 |
| PPIF | 1.778167e-04 | 0.000000e+00 | 9.578473e-06 | 1 | FALSE | FALSE | FALSE | FALSE | 67 | 80 |
| BRCA1 | 1.742907e-04 | 1.199398e-04 | -3.808969e-04 | 2 | FALSE | FALSE | FALSE | FALSE | 68 | 55 |
| SRSF3 | 1.632443e-04 | 0.000000e+00 | 1.503613e-04 | 1 | FALSE | FALSE | FALSE | FALSE | 69 | 68 |
| NOP56 | 1.548300e-04 | 0.000000e+00 | -2.461439e-04 | 1 | FALSE | FALSE | FALSE | FALSE | 70 | 61 |
| MYL6 | 1.541809e-04 | 0.000000e+00 | -5.557641e-04 | 1 | FALSE | FALSE | FALSE | FALSE | 71 | 46 |
| LGALS1 | 1.387697e-04 | 0.000000e+00 | 1.830989e-04 | 1 | FALSE | TRUE | FALSE | TRUE | 72 | 65 |
| PHGDH | 1.338904e-04 | 0.000000e+00 | 1.095444e-06 | 1 | FALSE | FALSE | FALSE | FALSE | 73 | 81 |
| AURKA | 1.316213e-04 | 0.000000e+00 | -3.628317e-05 | 1 | FALSE | FALSE | FALSE | FALSE | 74 | 75 |
| TUBB4B | 1.309983e-04 | 0.000000e+00 | 3.096596e-04 | 1 | FALSE | FALSE | FALSE | FALSE | 75 | 60 |
| STAT1 | 1.154839e-04 | 0.000000e+00 | 6.277068e-05 | 1 | TRUE | FALSE | FALSE | FALSE | 76 | 73 |
| IRF1 | 1.098877e-04 | 0.000000e+00 | -4.552711e-04 | 1 | TRUE | FALSE | FALSE | FALSE | 77 | 48 |
| GATA2 | 1.039767e-04 | 0.000000e+00 | -4.251870e-04 | 1 | TRUE | FALSE | FALSE | FALSE | 78 | 52 |
| FOS | 9.773594e-05 | 0.000000e+00 | -3.522839e-04 | 1 | TRUE | FALSE | FALSE | FALSE | 79 | 59 |
| CDK1 | 9.369986e-05 | 0.000000e+00 | -3.939987e-04 | 1 | FALSE | FALSE | FALSE | FALSE | 80 | 54 |
| FOXM1 | 9.080476e-05 | 0.000000e+00 | 1.835079e-04 | 1 | TRUE | FALSE | FALSE | FALSE | 81 | 64 |
In [805]:
read_dir
'/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new//main_PC9_LUNG_run10_t14_ncyc_cyc'
In [806]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [807]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [808]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [809]:
plot_df
| Gene | variable | value |
|---|---|---|
| <chr> | <chr> | <dbl> |
| ACTG2 | run0 | 0.0003696884 |
| ACTN1 | run0 | NA |
| AKR1C3 | run0 | 0.0028334604 |
| ANXA2 | run0 | 0.0012547938 |
| APP | run0 | 0.0012565161 |
| ARF4 | run0 | NA |
| ASF1B | run0 | 0.0003938598 |
| ATF3 | run0 | 0.0004590547 |
| ATF4 | run0 | NA |
| AURKA | run0 | 0.0013162135 |
| AURKB | run0 | 0.0006685557 |
| BARD1 | run0 | NA |
| BIN1 | run0 | NA |
| BIRC5 | run0 | NA |
| BRCA1 | run0 | 0.0007866431 |
| CALD1 | run0 | NA |
| CALR | run0 | 0.0012828062 |
| CCNA2 | run0 | NA |
| CCNB1 | run0 | 0.0004704217 |
| CCND1 | run0 | 0.0016369422 |
| CD24 | run0 | 0.0038024938 |
| CD44 | run0 | 0.0012120469 |
| CD59 | run0 | 0.0026735116 |
| CDC20 | run0 | NA |
| CDCA3 | run0 | NA |
| CDK1 | run0 | 0.0005936714 |
| CDKN3 | run0 | NA |
| CEBPB | run0 | 0.0008024935 |
| CEBPD | run0 | 0.0004966382 |
| CENPA | run0 | NA |
| ⋮ | ⋮ | ⋮ |
| PLK1 | run9 | 0.0002112278 |
| PLK2 | run9 | NA |
| PPIF | run9 | NA |
| PRDX1 | run9 | 0.0004295610 |
| PSMB1 | run9 | NA |
| PSMC4 | run9 | NA |
| PTTG1 | run9 | 0.0022282538 |
| RFC4 | run9 | NA |
| RRBP1 | run9 | NA |
| RRM2 | run9 | NA |
| SAT1 | run9 | 0.0052132864 |
| SERPINH1 | run9 | 0.0020251341 |
| SOD2 | run9 | NA |
| SPC25 | run9 | NA |
| SRSF3 | run9 | NA |
| STAT1 | run9 | 0.0011548388 |
| TCOF1 | run9 | 0.0002267744 |
| TOP2A | run9 | NA |
| TPM1 | run9 | 0.0015226273 |
| TPX2 | run9 | NA |
| TRIP13 | run9 | NA |
| TUBA1B | run9 | 0.0019481454 |
| TUBA4A | run9 | 0.0006985455 |
| TUBB4B | run9 | 0.0003493046 |
| TYMS | run9 | 0.0007126650 |
| UBB | run9 | 0.0003479973 |
| UBC | run9 | 0.0029201839 |
| UBE2C | run9 | 0.0019457745 |
| UCHL1 | run9 | 0.0039379844 |
| ZWINT | run9 | NA |
In [810]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [811]:
tmp <- plot_df[plot_df$Gene %in% gl,]
tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [812]:
gl
- 'IGFBP5'
- 'GSTM3'
- 'HLA-B'
- 'CD24'
- 'HIST1H4C'
- 'SAT1'
- 'ELF3'
- 'UCHL1'
- 'UBC'
- 'AKR1C3'
In [816]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
legend.position = 'none',
panel.grid = element_blank()
)
ridge_p
Picking joint bandwidth of 0.000677
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [817]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_ridge_p.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_ridge_p.png',
width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.000677 Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Picking joint bandwidth of 0.000677 Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.”
In [822]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [823]:
head(drivers_exp)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | time0_exp | time3_exp | time7_exp | time14_exp |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> | <dbl> | <dbl> |
| IGFBP5 | 0.011728111 | 0.0037999116 | 0.013004079 | 10 | FALSE | FALSE | FALSE | FALSE | -0.4614117 | -0.0006751149 | -0.24896230 | 0.19265650 |
| GSTM3 | 0.009201345 | 0.0016104131 | 0.004694919 | 10 | FALSE | FALSE | FALSE | FALSE | -0.4995057 | -0.4245171000 | -0.55196540 | 0.52083343 |
| HLA-B | 0.006611837 | 0.0014072035 | -0.003888358 | 10 | FALSE | FALSE | FALSE | FALSE | -1.3243920 | -0.1092498800 | -0.01360234 | 0.39492068 |
| CD24 | 0.004436760 | 0.0006784051 | 0.005839140 | 10 | FALSE | FALSE | FALSE | FALSE | -1.4788445 | 0.0453659200 | 0.24021030 | 0.27349910 |
| HIST1H4C | 0.004068935 | 0.0005085617 | -0.005728029 | 10 | FALSE | FALSE | FALSE | FALSE | 1.5583935 | -0.4402804400 | -0.44383672 | -0.02076823 |
| SAT1 | 0.003824356 | 0.0007941552 | -0.001987261 | 10 | FALSE | FALSE | FALSE | FALSE | -1.0865847 | 0.3337592500 | 0.26571733 | 0.01365761 |
In [824]:
drivers_exp_long<-drivers_exp %>%as.data.frame %>%
mutate(direction=ifelse(weight_grad_total_dir_mean > 0,'pos','neg')) %>%
mutate(rank=rank(-drivers_exp$weight_shap_total_mean)) %>%
mutate(top20 = ifelse(rank<=20,TRUE,FALSE)) %>%
mutate(top10 = ifelse(rank<=10,TRUE,FALSE)) %>%
dplyr::select(c('V1','time0_exp','time3_exp','time7_exp','time14_exp','direction','top20','top10')) %>%
reshape2::melt(id.vars = c("V1",'direction','top20','top10'), #需保留的不参与聚合的变量列名
measure.vars = c('time0_exp','time3_exp','time7_exp','time14_exp'),#需要聚合的变量s1-s10
variable.name = c('time_point'),#聚合变量的新列名
value.name = 'exp')#聚合值的新列名
In [825]:
head(drivers_exp_long)
| V1 | direction | top20 | top10 | time_point | exp | |
|---|---|---|---|---|---|---|
| <chr> | <chr> | <lgl> | <lgl> | <fct> | <dbl> | |
| 1 | IGFBP5 | pos | TRUE | TRUE | time0_exp | -0.4614117 |
| 2 | GSTM3 | pos | TRUE | TRUE | time0_exp | -0.4995057 |
| 3 | HLA-B | neg | TRUE | TRUE | time0_exp | -1.3243920 |
| 4 | CD24 | pos | TRUE | TRUE | time0_exp | -1.4788445 |
| 5 | HIST1H4C | neg | TRUE | TRUE | time0_exp | 1.5583935 |
| 6 | SAT1 | neg | TRUE | TRUE | time0_exp | -1.0865847 |
In [826]:
pos_drivers <- drivers_exp_long %>% filter(direction=='pos')
neg_drivers <- drivers_exp_long %>% filter(direction=='neg')
In [827]:
pos_time_p<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
geom_xspline(spline_shape = -0.5)+
geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
geom_point(size=3)+
geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#259CA2BB','FALSE'='#00000033'))+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
#ylim(-1.5,1.5)+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
pos_time_p
Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_x_spline()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_point()`).”
In [551]:
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pos_driver_cross_time_ggalt.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pos_driver_cross_time_ggalt.png',
width =16/1.5, height =9/1.5)
Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_x_spline()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_point()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_x_spline()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_point()`).”
In [828]:
neg_time_p<-ggplot(neg_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
geom_xspline(spline_shape = -0.5)+
geom_xspline(data = neg_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
geom_point(size=3)+
geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#E0A965BB','FALSE'='#00000033'))+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
neg_time_p
In [553]:
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_neg_driver_cross_time_ggalt.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_neg_driver_cross_time_ggalt.png',
width =16/1.5, height =9/1.5)
In [ ]:
In [189]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“4.94% of input gene IDs are fail to map...”
In [190]:
kk <- enrichKEGG(gene = gene$ENTREZID,
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.1)
head(kk)
Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"... Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...
| category | subcategory | ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| hsa05167 | Human Diseases | Infectious disease: viral | hsa05167 | Kaposi sarcoma-associated herpesvirus infection | 10/64 | 194/8764 | 1.200851e-06 | 0.0001682581 | 0.0001198649 | 3106/7316/595/3107/3105/4609/3091/3725/2353/6772 | 10 |
| hsa04218 | Cellular Processes | Cell growth and death | hsa04218 | Cellular senescence | 9/64 | 156/8764 | 1.699576e-06 | 0.0001682581 | 0.0001198649 | 3106/595/3107/3105/4605/4609/891/2305/983 | 9 |
| hsa05166 | Human Diseases | Infectious disease: viral | hsa05166 | Human T-cell leukemia virus 1 infection | 9/64 | 222/8764 | 2.995343e-05 | 0.0019769263 | 0.0014083367 | 3106/595/3107/3105/811/9232/4609/3725/2353 | 9 |
| hsa05169 | Human Diseases | Infectious disease: viral | hsa05169 | Epstein-Barr virus infection | 8/64 | 202/8764 | 1.014199e-04 | 0.0032744610 | 0.0023326836 | 3106/595/3107/3105/811/4609/3725/6772 | 8 |
| hsa05203 | Human Diseases | Cancer: overview | hsa05203 | Viral carcinogenesis | 8/64 | 204/8764 | 1.086389e-04 | 0.0032744610 | 0.0023326836 | 3106/595/3107/3105/5315/3725/2959/983 | 8 |
| hsa00010 | Metabolism | Carbohydrate metabolism | hsa00010 | Glycolysis / Gluconeogenesis | 5/64 | 67/8764 | 1.207803e-04 | 0.0032744610 | 0.0023326836 | 3945/2023/5315/5230/5214 | 5 |
In [829]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“4.94% of input gene IDs are fail to map...”
In [830]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, entrez_gene)
m_t2g$gs_name <- m_t2g$gs_name %>%
stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
stringr::str_to_title()
kk <- enrichKEGG(gene = names(gs),
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
In [831]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()
for (i in 1:nrow(em@result)){
if (i==1){
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [832]:
#ggplot2 修改图例的一些操作 https://zhuanlan.zhihu.com/p/166529941
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.322
In [833]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pRidge_H.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pRidge_H.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.322 Picking joint bandwidth of 0.322
In [834]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()
for (i in 1:nrow(kk@result[1:30,])){
if (i==1){
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [835]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'KEGG',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.276
In [836]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pRidge_K.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t14_cyc_pRidge_K.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.276 Picking joint bandwidth of 0.276
t0_cyc_ncyc¶
In [ ]:
In [837]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [838]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| PSMC4 | 0.0009237856 | 0.0003805723 | 0.003996823 | 10 | FALSE | FALSE | FALSE | FALSE |
| LMO7 | 0.0008511244 | 0.0001933136 | 0.018705904 | 10 | FALSE | FALSE | FALSE | FALSE |
| TGM2 | 0.0008014264 | 0.0002573693 | 0.002679401 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD24 | 0.0013381759 | 0.0002601511 | 0.006296788 | 10 | FALSE | FALSE | FALSE | FALSE |
| TPM1 | 0.0018060397 | 0.0003299156 | 0.008614676 | 10 | FALSE | FALSE | FALSE | FALSE |
| SRSF7 | 0.0012505469 | 0.0003662743 | -0.003673011 | 10 | FALSE | FALSE | FALSE | FALSE |
In [839]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [840]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
In [841]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [842]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [843]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [844]:
plot_df
| Gene | variable | value |
|---|---|---|
| <chr> | <chr> | <dbl> |
| BARD1 | run0 | 6.258099e-04 |
| CD24 | run0 | 1.343648e-03 |
| CD9 | run0 | NA |
| CEBPD | run0 | 1.352879e-03 |
| DAAM1 | run0 | 1.452751e-03 |
| GTF2B | run0 | NA |
| HSPA2 | run0 | 1.362294e-04 |
| KLF10 | run0 | 9.780429e-04 |
| KLF5 | run0 | 8.033533e-04 |
| LAMP2 | run0 | NA |
| LMO7 | run0 | 8.168148e-04 |
| MAFF | run0 | 1.181879e-03 |
| MSH6 | run0 | 7.886995e-04 |
| MYLK | run0 | 2.815358e-05 |
| MYO1B | run0 | 4.807452e-04 |
| PLK2 | run0 | 5.894743e-04 |
| PRDM1 | run0 | 1.185135e-04 |
| PSMC4 | run0 | 1.325408e-03 |
| PTPRK | run0 | NA |
| RAB31 | run0 | 1.050034e-03 |
| RRBP1 | run0 | 2.118842e-03 |
| SLC1A5 | run0 | 2.550872e-03 |
| SRSF7 | run0 | 9.063769e-04 |
| TFAP2C | run0 | 1.276508e-04 |
| TFDP1 | run0 | 1.033369e-03 |
| TGM2 | run0 | 1.200146e-03 |
| TPM1 | run0 | 2.229035e-03 |
| TRIB1 | run0 | NA |
| TRIB3 | run0 | 2.341409e-03 |
| TUBA1B | run0 | 1.615746e-03 |
| ⋮ | ⋮ | ⋮ |
| DAAM1 | run9 | 1.519394e-03 |
| GTF2B | run9 | NA |
| HSPA2 | run9 | 6.149456e-05 |
| KLF10 | run9 | 8.660538e-04 |
| KLF5 | run9 | 1.084358e-03 |
| LAMP2 | run9 | 8.435934e-04 |
| LMO7 | run9 | 1.018867e-03 |
| MAFF | run9 | 3.525586e-04 |
| MSH6 | run9 | 9.504799e-04 |
| MYLK | run9 | 1.742803e-05 |
| MYO1B | run9 | 6.943114e-04 |
| PLK2 | run9 | 5.458337e-04 |
| PRDM1 | run9 | 1.289089e-04 |
| PSMC4 | run9 | 6.307576e-04 |
| PTPRK | run9 | NA |
| RAB31 | run9 | NA |
| RRBP1 | run9 | 2.204840e-03 |
| SLC1A5 | run9 | 2.257197e-03 |
| SRSF7 | run9 | 1.257358e-03 |
| TFAP2C | run9 | 3.653187e-04 |
| TFDP1 | run9 | 5.367805e-04 |
| TGM2 | run9 | 6.890538e-04 |
| TPM1 | run9 | 1.446126e-03 |
| TRIB1 | run9 | 1.183589e-03 |
| TRIB3 | run9 | 1.785804e-03 |
| TUBA1B | run9 | 6.444304e-04 |
| TUBA4A | run9 | 1.007705e-03 |
| TUBB4B | run9 | 3.454001e-03 |
| ULK1 | run9 | 1.139592e-04 |
| WDR36 | run9 | 7.750876e-04 |
In [845]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [846]:
tmp <- plot_df[plot_df$Gene %in% gl,]
tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [847]:
gl
- 'TUBB4B'
- 'SLC1A5'
- 'TRIB3'
- 'RRBP1'
- 'TPM1'
- 'TUBA1B'
- 'DAAM1'
- 'CD24'
- 'KLF5'
- 'KLF10'
In [848]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
legend.position = 'none',
panel.grid = element_blank()
)
ridge_p
Picking joint bandwidth of 0.000171
Warning message:
“Removed 1 row containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [849]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_ridge_p.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_ridge_p.png',
width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.000171 Warning message: “Removed 1 row containing non-finite outside the scale range (`stat_density_ridges()`).” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Picking joint bandwidth of 0.000171 Warning message: “Removed 1 row containing non-finite outside the scale range (`stat_density_ridges()`).” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.”
In [850]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [851]:
head(drivers_exp)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | time0_exp | time3_exp | time7_exp | time14_exp |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> | <dbl> | <dbl> |
| TUBB4B | 0.003762336 | 0.0004508041 | 0.013960897 | 10 | FALSE | FALSE | FALSE | FALSE | 0.02809229 | -0.3676338 | -0.2782917 | 0.274052020 |
| SLC1A5 | 0.002453577 | 0.0003867980 | 0.008121839 | 10 | FALSE | FALSE | FALSE | FALSE | 0.10378601 | -0.1310962 | -0.2516212 | 0.120953940 |
| TRIB3 | 0.001945853 | 0.0002947887 | -0.006270785 | 8 | FALSE | FALSE | FALSE | FALSE | 0.24430865 | 0.1077166 | -0.1700614 | -0.066484846 |
| RRBP1 | 0.001894564 | 0.0004346339 | 0.007122352 | 10 | FALSE | FALSE | FALSE | FALSE | -0.21148947 | -0.2455256 | 0.5670268 | 0.009739888 |
| TPM1 | 0.001806040 | 0.0003299156 | 0.008614676 | 10 | FALSE | FALSE | FALSE | FALSE | -0.77242580 | 0.3227159 | 0.4304303 | -0.109920340 |
| TUBA1B | 0.001504760 | 0.0003803525 | 0.007872457 | 10 | FALSE | FALSE | FALSE | FALSE | 1.04742420 | -0.4294127 | -0.4524028 | 0.104396370 |
In [852]:
drivers_exp_long<-drivers_exp %>%as.data.frame %>%
mutate(direction=ifelse(weight_grad_total_dir_mean > 0,'pos','neg')) %>%
mutate(rank=rank(-drivers_exp$weight_shap_total_mean)) %>%
mutate(top20 = ifelse(rank<=20,TRUE,FALSE)) %>%
mutate(top10 = ifelse(rank<=10,TRUE,FALSE)) %>%
dplyr::select(c('V1','time0_exp','time3_exp','time7_exp','time14_exp','direction','top20','top10')) %>%
reshape2::melt(id.vars = c("V1",'direction','top20','top10'), #需保留的不参与聚合的变量列名
measure.vars = c('time0_exp','time3_exp','time7_exp','time14_exp'),#需要聚合的变量s1-s10
variable.name = c('time_point'),#聚合变量的新列名
value.name = 'exp')#聚合值的新列名
In [853]:
head(drivers_exp_long)
| V1 | direction | top20 | top10 | time_point | exp | |
|---|---|---|---|---|---|---|
| <chr> | <chr> | <lgl> | <lgl> | <fct> | <dbl> | |
| 1 | TUBB4B | pos | TRUE | TRUE | time0_exp | 0.02809229 |
| 2 | SLC1A5 | pos | TRUE | TRUE | time0_exp | 0.10378601 |
| 3 | TRIB3 | neg | TRUE | TRUE | time0_exp | 0.24430865 |
| 4 | RRBP1 | pos | TRUE | TRUE | time0_exp | -0.21148947 |
| 5 | TPM1 | pos | TRUE | TRUE | time0_exp | -0.77242580 |
| 6 | TUBA1B | pos | TRUE | TRUE | time0_exp | 1.04742420 |
In [854]:
pos_drivers <- drivers_exp_long %>% filter(direction=='pos')
neg_drivers <- drivers_exp_long %>% filter(direction=='neg')
In [855]:
pos_time_p<-ggplot(pos_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
geom_xspline(spline_shape = -0.5)+
geom_xspline(data = pos_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
ggrepel::geom_text_repel(data=pos_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
geom_point(size=3)+
geom_point(data = pos_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#259CA2BB','FALSE'='#00000033'))+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
#ylim(-1.5,1.5)+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.6,1.6))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
pos_time_p
In [551]:
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pos_driver_cross_time_ggalt.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = pos_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pos_driver_cross_time_ggalt.png',
width =16/1.5, height =9/1.5)
Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_x_spline()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_point()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_x_spline()`).” Warning message: “Removed 1 row containing missing values or values outside the scale range (`geom_point()`).”
In [856]:
neg_time_p<-ggplot(neg_drivers%>% filter(!top20),aes(x=time_point,y=exp,group = V1,color = top20))+
geom_xspline(spline_shape = -0.5)+
geom_xspline(data = neg_drivers%>% filter(top20),spline_shape = -0.5,size=1.5)+
ggrepel::geom_text_repel(data=neg_drivers%>% filter(top10) %>% subset(time_point=='time0_exp'),
aes(label=V1),size=3, nudge_x = -0.25, segment.color = 'black')+
geom_point(size=3)+
geom_point(data = neg_drivers%>% filter(top20),size=4.5,shape=21,fill = 'white', stroke = 1.5)+
xlab('Time points (Days)')+
ylab('Normalized expression')+
ggtitle('Time-course gene expression')+
theme_bw()+
scale_color_manual(values = c('TRUE'= '#E0A965BB','FALSE'='#00000033'))+
scale_x_discrete(labels = c("time0_exp" = "0","time3_exp" = "3", "time7_exp" = "7","time14_exp" = "14"))+
scale_y_continuous(breaks=seq(-1.5, 1.5, 0.5),limits = c(-1.7,1.7))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = -2),
axis.text.y = element_text(hjust = -1.5),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(-10,'points'),
axis.line = element_line(linewidth = 1),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_rect(linewidth = 1.5),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.position = 'None')
neg_time_p
In [553]:
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_neg_driver_cross_time_ggalt.pdf',
width =16/1.5, height =9/1.5)
ggsave(plot = neg_time_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_neg_driver_cross_time_ggalt.png',
width =16/1.5, height =9/1.5)
In [ ]:
In [858]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
'select()' returned 1:1 mapping between keys and columns
In [859]:
kk <- enrichKEGG(gene = gene$ENTREZID,
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.1)
head(kk)
| category | subcategory | ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| hsa05016 | Human Diseases | Neurodegenerative disease | hsa05016 | Huntington disease | 6/24 | 306/8776 | 0.0001353240 | 0.003618933 | 0.002798745 | 5704/7052/10376/10383/7277/8408 | 6 |
| hsa04814 | Cellular Processes | Cell motility | hsa04814 | Motor proteins | 5/24 | 193/8776 | 0.0001477116 | 0.003618933 | 0.002798745 | 7168/10376/10383/4430/7277 | 5 |
| hsa05014 | Human Diseases | Neurodegenerative disease | hsa05014 | Amyotrophic lateral sclerosis | 6/24 | 364/8776 | 0.0003482132 | 0.005687482 | 0.004398482 | 5704/6432/10376/10383/7277/8408 | 6 |
| hsa04540 | Cellular Processes | Cellular community - eukaryotes | hsa04540 | Gap junction | 3/24 | 88/8776 | 0.0016932612 | 0.018368772 | 0.014205710 | 10376/10383/7277 | 3 |
| hsa05130 | Human Diseases | Infectious disease: bacterial | hsa05130 | Pathogenic Escherichia coli infection | 4/24 | 198/8776 | 0.0018743645 | 0.018368772 | 0.014205710 | 10376/10383/4430/7277 | 4 |
| hsa05010 | Human Diseases | Neurodegenerative disease | hsa05010 | Alzheimer disease | 5/24 | 384/8776 | 0.0033339461 | 0.027227227 | 0.021056502 | 5704/10376/10383/7277/8408 | 5 |
In [860]:
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
In [861]:
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
head(em)
| ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| Mtorc1 Signaling | Mtorc1 Signaling | Mtorc1 Signaling | 5/21 | 200/4383 | 0.002103942 | 0.02840321 | 0.02436143 | 928/5704/6510/57761/7277 | 5 |
| Tnfa Signaling Via Nfkb | Tnfa Signaling Via Nfkb | Tnfa Signaling Via Nfkb | 5/21 | 200/4383 | 0.002103942 | 0.02840321 | 0.02436143 | 1052/7071/23764/10769/10221 | 5 |
| Estrogen Response Early | Estrogen Response Early | Estrogen Response Early | 4/21 | 200/4383 | 0.013661695 | 0.12295526 | 0.10545870 | 7071/11031/7022/7052 | 4 |
| Tgf Beta Signaling | Tgf Beta Signaling | Tgf Beta Signaling | 2/21 | 54/4383 | 0.026939416 | 0.18184106 | 0.15596504 | 7071/11031 | 2 |
| Cholesterol Homeostasis | Cholesterol Homeostasis | Cholesterol Homeostasis | 2/21 | 74/4383 | 0.048022115 | 0.22941889 | 0.19677254 | 928/57761 | 2 |
| Il2 Stat5 Signaling | Il2 Stat5 Signaling | Il2 Stat5 Signaling | 3/21 | 199/4383 | 0.067161253 | 0.22941889 | 0.19677254 | 23764/6510/7052 | 3 |
In [862]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()
for (i in 1:nrow(em@result)){
if (i==1){
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
length(table(high_plot_df$gs))
table(high_plot_df$gs)
27
Adipogenesis Cholesterol Homeostasis
1 2
Coagulation Complement
2 1
Dna Repair E2f Targets
1 1
Epithelial Mesenchymal Transition Estrogen Response Early
2 4
Estrogen Response Late G2m Checkpoint
3 2
Glycolysis Heme Metabolism
1 1
Hypoxia Il2 Stat5 Signaling
2 3
Il6 Jak Stat3 Signaling Kras Signaling Dn
1 1
Kras Signaling Up Mitotic Spindle
2 1
Mtorc1 Signaling Myc Targets V1
5 3
Myogenesis P53 Pathway
1 2
Pi3k Akt Mtor Signaling Tgf Beta Signaling
1 2
Tnfa Signaling Via Nfkb Uv Response Up
5 1
Xenobiotic Metabolism
1
In [348]:
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
add
| weight_shap_total_mean | gs | |
|---|---|---|
| <dbl> | <chr> | |
| 72 | 1.284743e-03 | HALLMARK_TGF_BETA_SIGNALING |
| 171 | 9.876715e-04 | HALLMARK_TGF_BETA_SIGNALING |
| 261 | 1.945853e-03 | HALLMARK_CHOLESTEROL_HOMEOSTASIS |
| 31 | 5.934694e-05 | HALLMARK_CHOLESTEROL_HOMEOSTASIS |
| 102 | 5.009635e-04 | HALLMARK_COAGULATION |
| 33 | 5.934694e-05 | HALLMARK_COAGULATION |
| 24 | 1.806040e-03 | HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION |
| 232 | 8.014264e-04 | HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION |
| 221 | 8.638531e-04 | HALLMARK_G2M_CHECKPOINT |
| 1 | 5.255194e-04 | HALLMARK_G2M_CHECKPOINT |
| 233 | 8.014264e-04 | HALLMARK_HYPOXIA |
| 103 | 5.009635e-04 | HALLMARK_HYPOXIA |
| 251 | 2.708228e-04 | HALLMARK_KRAS_SIGNALING_UP |
| 14 | 1.475505e-04 | HALLMARK_KRAS_SIGNALING_UP |
| 262 | 1.945853e-03 | HALLMARK_P53_PATHWAY |
| 131 | 7.297564e-04 | HALLMARK_P53_PATHWAY |
| 34 | 5.934694e-05 | HALLMARK_IL6_JAK_STAT3_SIGNALING |
| 263 | 1.945853e-03 | HALLMARK_PI3K_AKT_MTOR_SIGNALING |
| 6 | 1.071527e-04 | HALLMARK_DNA_REPAIR |
| 281 | 4.270763e-04 | HALLMARK_UV_RESPONSE_UP |
| 282 | 4.270763e-04 | HALLMARK_MITOTIC_SPINDLE |
| 192 | 2.453577e-03 | HALLMARK_ADIPOGENESIS |
| 104 | 5.009635e-04 | HALLMARK_COMPLEMENT |
| 11 | 5.255194e-04 | HALLMARK_E2F_TARGETS |
| 152 | 9.237856e-04 | HALLMARK_GLYCOLYSIS |
| 5 | 1.481077e-03 | HALLMARK_HEME_METABOLISM |
| 12 | 5.255194e-04 | HALLMARK_KRAS_SIGNALING_DN |
| 8 | 1.301767e-03 | HALLMARK_MYOGENESIS |
| 193 | 2.453577e-03 | HALLMARK_XENOBIOTIC_METABOLISM |
In [349]:
high_plot_df <- rbind(high_plot_df,add,add)
length(table(high_plot_df$gs))
table(high_plot_df$gs)
27
HALLMARK_ADIPOGENESIS
3
HALLMARK_CHOLESTEROL_HOMEOSTASIS
6
HALLMARK_COAGULATION
6
HALLMARK_COMPLEMENT
3
HALLMARK_DNA_REPAIR
3
HALLMARK_E2F_TARGETS
3
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION
6
HALLMARK_ESTROGEN_RESPONSE_EARLY
4
HALLMARK_ESTROGEN_RESPONSE_LATE
3
HALLMARK_G2M_CHECKPOINT
6
HALLMARK_GLYCOLYSIS
3
HALLMARK_HEME_METABOLISM
3
HALLMARK_HYPOXIA
6
HALLMARK_IL2_STAT5_SIGNALING
3
HALLMARK_IL6_JAK_STAT3_SIGNALING
3
HALLMARK_KRAS_SIGNALING_DN
3
HALLMARK_KRAS_SIGNALING_UP
6
HALLMARK_MITOTIC_SPINDLE
3
HALLMARK_MTORC1_SIGNALING
5
HALLMARK_MYC_TARGETS_V1
3
HALLMARK_MYOGENESIS
3
HALLMARK_P53_PATHWAY
6
HALLMARK_PI3K_AKT_MTOR_SIGNALING
3
HALLMARK_TGF_BETA_SIGNALING
6
HALLMARK_TNFA_SIGNALING_VIA_NFKB
5
HALLMARK_UV_RESPONSE_UP
3
HALLMARK_XENOBIOTIC_METABOLISM
3
In [350]:
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
head(high_plot_df)
| gs | weight_shap_total_mean | mean | |
|---|---|---|---|
| <chr> | <dbl> | <dbl> | |
| 1 | HALLMARK_ADIPOGENESIS | 0.002453577 | 0.002453577 |
| 2 | HALLMARK_ADIPOGENESIS | 0.002453577 | 0.002453577 |
| 3 | HALLMARK_ADIPOGENESIS | 0.002453577 | 0.002453577 |
| 4 | HALLMARK_CHOLESTEROL_HOMEOSTASIS | 0.001945853 | 0.001002600 |
| 5 | HALLMARK_CHOLESTEROL_HOMEOSTASIS | 0.001945853 | 0.001002600 |
| 6 | HALLMARK_CHOLESTEROL_HOMEOSTASIS | 0.001945853 | 0.001002600 |
In [351]:
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
In [352]:
colnames(high_plot_df) <- c('gs','Weight','mean')
In [353]:
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = ' ') +
scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.text.y = element_text(size = 10,color="black"),
axis.text.x = element_text(size = 12,color="black"),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
)
pRidge
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.539
In [354]:
png('weight_pRidge_all_meancolor_t0_c_nc.png',height = 500*5,width = 600*5,res=400)
pRidge
dev.off()
Picking joint bandwidth of 0.539
png: 2

In [863]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns
In [864]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, entrez_gene)
m_t2g$gs_name <- m_t2g$gs_name %>%
stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
stringr::str_to_title()
kk <- enrichKEGG(gene = names(gs),
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
In [865]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()
for (i in 1:nrow(em@result)){
if (i==1){
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [866]:
#ggplot2 修改图例的一些操作 https://zhuanlan.zhihu.com/p/166529941
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.539
In [867]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pRidge_H.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pRidge_H.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.539 Picking joint bandwidth of 0.539
In [868]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()
for (i in 1:nrow(kk@result[1:30,])){
if (i==1){
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [869]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'KEGG',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.52
In [870]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pRidge_K.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/t0_cyc_pRidge_K.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.52 Picking joint bandwidth of 0.52
In [ ]:
build pip¶
In [492]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
In [525]:
plot_df$pw = 'Other'
plot_df[plot_df$Gene %in% pathway_hallmark$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY,]$pw <- 'ROS'
plot_df[plot_df$Gene %in% pathway_hallmark$HALLMARK_FATTY_ACID_METABOLISM,]$pw <- 'FAM'
plot_df$pw <- factor(plot_df$pw ,levels = c('Other','ROS','FAM'))
In [526]:
ggplot(data = plot_df)+
geom_boxplot(aes(x=variable,y=value,fill=pw))+
theme_classic()
Warning message: “Removed 408 rows containing non-finite outside the scale range (`stat_boxplot()`).”
In [404]:
normalize <- function(v) {
(v - min(v)) / (max(v) - min(v))
}
In [541]:
drivers$norm_shap <- normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = ddf$V1)
In [542]:
ggplot(ddf) +
geom_bar(aes(x = V1, y = norm_shap),stat = "identity",fill=rgb(0.1,0.4,0.5,0.7))+
theme_classic()
In [544]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [545]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [563]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
plot_df <- na.omit(plot_df)
plot_df$value <- normalize(plot_df$value)
gl <- ddf$V1
tmp <- plot_df[plot_df$Gene %in% gl,]
tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [564]:
ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
theme(legend.position = "none")
Picking joint bandwidth of 0.0311
In [568]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“6.35% of input gene IDs are fail to map...”
In [575]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, entrez_gene)
kk <- enrichKEGG(gene = names(gs),
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
In [578]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()
for (i in 1:nrow(em@result)){
if (i==1){
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [580]:
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = ' ') +
scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.text.y = element_text(size = 10,color="black"),
axis.text.x = element_text(size = 12,color="black"),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
)
pRidge
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.487
In [588]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()
for (i in 1:nrow(kk@result[1:30,])){
if (i==1){
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [589]:
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = ' ') +
scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.text.y = element_text(size = 10,color="black"),
axis.text.x = element_text(size = 12,color="black"),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
)
pRidge
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.4
In [ ]:
pdf('weight_pRidge_all_meancolor_t0_c_nc.pdf',height = 500*5,width = 600*5)
pRidge
dev.off()
In [167]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% select(c('V1','weight_shap_total'))
In [168]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [169]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [668]:
normalize <- function(v) {
(v - min(v)) / (max(v) - min(v))
}
draw_ridge <- function(em,ags,name,cut=30){
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
result <- em@result
if (nrow(result) >=30){
result <- result[1:30,]
}
gs_name <- result$Description
high_mean_ls <-c()
for (i in 1:nrow(result)){
if (i==1){
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
pRidge <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = ' ') +
scale_fill_gradientn(name = "Median",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.text.y = element_text(size = 10,color="black"),
axis.text.x = element_text(size = 12,color="black"),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank() # get rid of minor grid
)
#pdf(paste0(name,'_weight_pRidge_all_meancolor.pdf'),height = 5,width = 6)
#pRidge
#dev.off()
ggsave(pRidge,filename = paste0(name,'_weight_pRidge_all_meancolor.pdf'),height = 250/30.48,width = 300/30.48)
}
pipe_n <- function(run_name){
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
#run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
print(drivers[drivers$is_in_Pathway,])
pathway_hallmark = openxlsx::read.xlsx(file.path('./resources/pathway', 'Gene_signature_list_paper_supp.xlsx'))
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
plot_df$pw = 'Other'
if (any(drivers$is_in_FAM) & any(drivers$is_in_ROS)){
plot_df[plot_df$Gene %in% pathway_hallmark$HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY,]$pw <- 'ROS'
plot_df[plot_df$Gene %in% pathway_hallmark$HALLMARK_FATTY_ACID_METABOLISM,]$pw <- 'FAM'
plot_df$pw <- factor(plot_df$pw ,levels = c('Other','ROS','FAM'))
p <- ggplot(data = plot_df)+
geom_boxplot(aes(x=variable,y=value,fill=pw))+
theme_classic()
ggsave(p,filename = paste0(run_name,'_dirver_in_pw.pdf'),height = 5,width = 6)
}
drivers$norm_shap <- normalize(drivers$weight_shap_total_mean)
ddf <- drivers[order(drivers$weight_shap_total_mean,decreasing = T),][1:10,] %>% as.data.frame()
ddf$V1 <- factor(ddf$V1,levels = ddf$V1)
p <- ggplot(ddf) +
geom_bar(aes(x = V1, y = norm_shap),stat = "identity",fill=rgb(0.1,0.4,0.5,0.7))+
theme_classic()
ggsave(p,filename = paste0(run_name,'_dirver_top10.pdf'),height = 5,width = 6)
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
plot_df <- na.omit(plot_df)
plot_df$value <- normalize(plot_df$value)
gl <- ddf$V1
tmp <- plot_df[plot_df$Gene %in% gl,]
tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
theme(legend.position = "none")
ggsave(p,filename = paste0(run_name,'_dirver_top10_inrun.pdf'),height = 5,width = 5)
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, entrez_gene)
kk <- enrichKEGG(gene = names(gs),
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
draw_ridge(em,ags,name = paste0(run_name,'_HALLMARK'))
draw_ridge(kk,ags,name = paste0(run_name,'_KEGG'))
}
In [669]:
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
pipe_n(run_name)
V1 weight_shap_total_mean weight_shap_total_std
<char> <num> <num>
1: H2AFZ 0.001409551 0.0002377385
2: PRDX1 0.005753293 0.0005948305
3: NQO1 0.007189172 0.0005834166
4: ALDH3A1 0.001305538 0.0001966169
weight_grad_total_dir_mean counts is_tf is_in_FAM is_in_ROS is_in_Pathway
<num> <int> <lgcl> <lgcl> <lgcl> <lgcl>
1: 4.583457e-05 10 FALSE TRUE FALSE TRUE
2: 1.307083e-04 10 FALSE FALSE TRUE TRUE
3: 1.745988e-04 10 FALSE FALSE TRUE TRUE
4: 4.354592e-07 4 FALSE TRUE FALSE TRUE
rank_shap_weight rank_grad_weight
<num> <num>
1: 37 41
2: 5 9
3: 3 3
4: 39 49
Warning message:
“Removed 408 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Picking joint bandwidth of 0.0311
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“6.35% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.467
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.4
In [670]:
run_name <- 'main_PC9_LUNG_run10_t7_t14_cyc'
pipe_n(run_name)
V1 weight_shap_total_mean weight_shap_total_std
<char> <num> <num>
1: PRDX1 0.003196059 0.0005124655
2: H2AFZ 0.004075658 0.0005847643
3: ALDH3A1 0.004452039 0.0005462090
4: NQO1 0.002075033 0.0002167071
weight_grad_total_dir_mean counts is_tf is_in_FAM is_in_ROS is_in_Pathway
<num> <int> <lgcl> <lgcl> <lgcl> <lgcl>
1: 0.0004087128 10 FALSE FALSE TRUE TRUE
2: 0.0006475787 10 FALSE TRUE FALSE TRUE
3: 0.0007624793 10 FALSE TRUE FALSE TRUE
4: 0.0003687808 9 FALSE FALSE TRUE TRUE
rank_shap_weight rank_grad_weight
<num> <num>
1: 13 22
2: 8 8
3: 6 5
4: 23 24
Warning message:
“Removed 398 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Picking joint bandwidth of 0.021
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“1.67% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.344
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.265
In [671]:
run_name <- 'main_PC9_LUNG_run10_t14_ncyc_cyc'
pipe_n(run_name)
V1 weight_shap_total_mean weight_shap_total_std
<char> <num> <num>
1: H2AFZ 0.0015434791 0.0003817574
2: JUNB 0.0016835371 0.0008096141
3: SOD2 0.0002215576 0.0000000000
4: LGALS1 0.0001387697 0.0000000000
5: PFKP 0.0002421256 0.0000000000
weight_grad_total_dir_mean counts is_tf is_in_FAM is_in_ROS is_in_Pathway
<num> <int> <lgcl> <lgcl> <lgcl> <lgcl>
1: 0.0015961908 10 FALSE TRUE FALSE TRUE
2: -0.0029814138 7 TRUE FALSE TRUE TRUE
3: 0.0007589676 1 FALSE FALSE TRUE TRUE
4: 0.0001830989 1 FALSE TRUE FALSE TRUE
5: 0.0002091714 1 FALSE FALSE TRUE TRUE
rank_shap_weight rank_grad_weight
<num> <num>
1: 22 20
2: 20 9
3: 63 39
4: 72 65
5: 61 63
Warning message:
“Removed 666 rows containing non-finite outside the scale range
(`stat_boxplot()`).”
Picking joint bandwidth of 0.0387
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“4.94% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.326
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.276
In [672]:
run_name <- 'main_PC9_LUNG_run10_t0_ncyc_cyc'
pipe_n(run_name)
Empty data.table (0 rows and 11 cols): V1,weight_shap_total_mean,weight_shap_total_std,weight_grad_total_dir_mean,counts,is_tf...
Picking joint bandwidth of 0.0387 'select()' returned 1:1 mapping between keys and columns Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.539 Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.52
In [673]:
run_name <- 'main_PC9_LUNG_run10_t0_sen_res'
pipe_n(run_name)
Empty data.table (0 rows and 11 cols): V1,weight_shap_total_mean,weight_shap_total_std,weight_grad_total_dir_mean,counts,is_tf...
Picking joint bandwidth of 0.049
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“3.85% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.588
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.592
In [674]:
run_name <- 'ex_PC9_LUNG_run10_ncyc_cyc'
pipe_n(run_name)
V1 weight_shap_total_mean weight_shap_total_std
<char> <num> <num>
1: JUNB 0.001167002 0.0003537751
weight_grad_total_dir_mean counts is_tf is_in_FAM is_in_ROS is_in_Pathway
<num> <int> <lgcl> <lgcl> <lgcl> <lgcl>
1: -0.0005070834 10 TRUE FALSE TRUE TRUE
rank_shap_weight rank_grad_weight
<num> <num>
1: 5 16
Picking joint bandwidth of 0.0559 'select()' returned 1:1 mapping between keys and columns Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.422 Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.413
In [675]:
run_name <- 'ex_HCC827_LUNG_run10_ncyc_cyc'
pipe_n(run_name)
Empty data.table (0 rows and 11 cols): V1,weight_shap_total_mean,weight_shap_total_std,weight_grad_total_dir_mean,counts,is_tf...
Warning message: “Removed 2 rows containing missing values or values outside the scale range (`geom_bar()`).” Picking joint bandwidth of 0.0206 'select()' returned 1:1 mapping between keys and columns Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.666 Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.704
In [676]:
run_name <- 'main_PC9_LUNG_run10_t0_sen_res_strict'
pipe_n(run_name)
Empty data.table (0 rows and 11 cols): V1,weight_shap_total_mean,weight_shap_total_std,weight_grad_total_dir_mean,counts,is_tf...
Picking joint bandwidth of 0.0422
'select()' returned 1:1 mapping between keys and columns
Warning message in bitr(drivers$V1, fromType = "SYMBOL", toType = c("ENTREZID"), :
“3.33% of input gene IDs are fail to map...”
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.539
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6):
“Ignoring unknown parameters: `size`”
Picking joint bandwidth of 0.465
In [ ]:
add new func¶
In [460]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
In [461]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| FN1 | 0.007993378 | 0.0007537607 | 1.907695e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| HIST1H2BD | 0.003864827 | 0.0005008594 | 1.813786e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.002306744 | 0.0002728280 | 1.020445e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| KRT8 | 0.004171373 | 0.0006077977 | 7.349687e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.006570227 | 0.0004776801 | 1.470234e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD9 | 0.004286982 | 0.0004431204 | 1.179431e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
In [462]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [463]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE | 37 | 41 |
| PRDX1 | 0.005753293 | 0.0005948305 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 5 | 9 |
| NQO1 | 0.007189172 | 0.0005834166 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 3 | 3 |
| ALDH3A1 | 0.001305538 | 0.0001966169 | 4.354592e-07 | 4 | FALSE | TRUE | FALSE | TRUE | 39 | 49 |
In [466]:
drivers %>% mutate(direction = ifelse(weight_grad_total_dir_mean >= 0,1,-1)) %>% filter(counts >=10)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight | direction |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> | <dbl> |
| FN1 | 0.007993378 | 0.0007537607 | 1.907695e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 2 | 1 | 1 |
| HIST1H2BD | 0.003864827 | 0.0005008594 | 1.813786e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 14 | 2 | 1 |
| CCND1 | 0.002306744 | 0.0002728280 | 1.020445e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 27 | 18 | 1 |
| KRT8 | 0.004171373 | 0.0006077977 | 7.349687e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 12 | 30 | 1 |
| GSTM3 | 0.006570227 | 0.0004776801 | 1.470234e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 4 | 8 | 1 |
| CD9 | 0.004286982 | 0.0004431204 | 1.179431e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 10 | 14 | 1 |
| H2AFZ | 0.001409551 | 0.0002377385 | 4.583457e-05 | 10 | FALSE | TRUE | FALSE | TRUE | 37 | 41 | 1 |
| LAPTM4A | 0.005557391 | 0.0006989103 | 1.191966e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 6 | 13 | 1 |
| MDK | 0.004485494 | 0.0007073663 | 9.334373e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 9 | 23 | 1 |
| PRDX1 | 0.005753293 | 0.0005948305 | 1.307083e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 5 | 9 | 1 |
| PGK1 | 0.002937007 | 0.0004105406 | 8.659618e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 20 | 25 | 1 |
| NQO1 | 0.007189172 | 0.0005834166 | 1.745988e-04 | 10 | FALSE | FALSE | TRUE | TRUE | 3 | 3 | 1 |
| ALPP | 0.003790364 | 0.0003220504 | 1.560096e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 15 | 6 | 1 |
| MT2A | 0.002992815 | 0.0007070334 | -5.982608e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 19 | 36 | -1 |
| PKM | 0.002418476 | 0.0004418046 | 8.013423e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 26 | 27 | 1 |
| UBB | 0.002093029 | 0.0004443382 | -5.899295e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 30 | 37 | -1 |
| FOSL1 | 0.004265769 | 0.0005058976 | -9.429040e-05 | 10 | TRUE | FALSE | FALSE | FALSE | 11 | 21 | -1 |
| CTSA | 0.004027971 | 0.0005851937 | 1.295741e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 13 | 10 | 1 |
| UBE2S | 0.004784057 | 0.0005292148 | -1.516910e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 8 | 7 | -1 |
| HSPB1 | 0.002017296 | 0.0003566292 | 7.373099e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 31 | 29 | 1 |
| MYL6 | 0.008508458 | 0.0008462955 | 1.682113e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 1 | 4 | 1 |
| TCOF1 | 0.002286529 | 0.0003146553 | -6.928714e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 28 | 32 | -1 |
| TFDP1 | 0.003330428 | 0.0003013794 | -9.927206e-05 | 10 | TRUE | FALSE | FALSE | FALSE | 17 | 19 | -1 |
| CSTB | 0.003148289 | 0.0003750370 | -1.198390e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 18 | 12 | -1 |
| JUND | 0.001684324 | 0.0003056773 | 1.057126e-04 | 10 | TRUE | FALSE | FALSE | FALSE | 33 | 17 | 1 |
| TGM2 | 0.005329582 | 0.0004317851 | -1.178054e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 7 | 15 | -1 |
| SQSTM1 | 0.002869604 | 0.0004758771 | 9.440278e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 21 | 20 | 1 |
| HIST1H4C | 0.003751588 | 0.0004830967 | -1.076414e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 16 | 16 | -1 |
In [ ]:
ext lung¶
In [119]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'ex_PC9_LUNG_run10_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [120]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| KRT8 | 0.0029012616 | 0.0003359210 | 0.0013636484 | 10 | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.0005943084 | 0.0002472813 | 0.0002804691 | 10 | FALSE | FALSE | FALSE | FALSE |
| IGFBP3 | 0.0017675749 | 0.0005854681 | -0.0004515210 | 10 | FALSE | FALSE | FALSE | FALSE |
| FOS | 0.0008979096 | 0.0003130372 | -0.0005937169 | 10 | TRUE | FALSE | FALSE | FALSE |
| NDRG1 | 0.0009067307 | 0.0004197639 | -0.0005180585 | 10 | FALSE | FALSE | FALSE | FALSE |
| MCM7 | 0.0014532326 | 0.0003902143 | -0.0007466146 | 10 | FALSE | FALSE | FALSE | FALSE |
In [65]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [66]:
drivers[drivers$is_in_Pathway,]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| JUNB | 0.001167002 | 0.0003537751 | -0.0005070834 | 10 | TRUE | FALSE | TRUE | TRUE | 5 | 16 |
In [80]:
drivers[order(weight_shap_total_mean,decreasing = T),]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| KRT8 | 2.901262e-03 | 3.359210e-04 | 1.363648e-03 | 10 | FALSE | FALSE | FALSE | FALSE | 1 | 1 |
| IGFBP3 | 1.767575e-03 | 5.854681e-04 | -4.515210e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 2 | 18 |
| SVIP | 1.605107e-03 | 2.204604e-04 | -6.380635e-04 | 9 | FALSE | FALSE | FALSE | FALSE | 3 | 8 |
| MCM7 | 1.453233e-03 | 3.902143e-04 | -7.466146e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 4 | 4 |
| JUNB | 1.167002e-03 | 3.537751e-04 | -5.070834e-04 | 10 | TRUE | FALSE | TRUE | TRUE | 5 | 16 |
| LMO7 | 1.146797e-03 | 4.774872e-04 | -3.694111e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 6 | 20 |
| TGM2 | 1.072440e-03 | 3.278384e-04 | -9.264323e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 7 | 28 |
| NDRG1 | 9.067307e-04 | 4.197639e-04 | -5.180585e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 8 | 15 |
| FOS | 8.979096e-04 | 3.130372e-04 | -5.937169e-04 | 10 | TRUE | FALSE | FALSE | FALSE | 9 | 11 |
| TANK | 8.252715e-04 | 4.200121e-04 | -7.183048e-04 | 9 | FALSE | FALSE | FALSE | FALSE | 10 | 6 |
| CEBPD | 7.429256e-04 | 1.433738e-04 | -7.833183e-04 | 10 | TRUE | FALSE | FALSE | FALSE | 11 | 3 |
| E2F1 | 6.951700e-04 | 3.402180e-04 | 6.151628e-04 | 8 | TRUE | FALSE | FALSE | FALSE | 12 | 9 |
| HMOX1 | 6.298396e-04 | 1.902128e-04 | -5.331388e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 13 | 14 |
| CCND1 | 5.943084e-04 | 2.472813e-04 | 2.804691e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 14 | 24 |
| CDKN1A | 5.431046e-04 | 8.991307e-05 | -8.614744e-04 | 9 | FALSE | FALSE | FALSE | FALSE | 15 | 2 |
| SIAH1 | 3.769688e-04 | 5.853661e-04 | -6.948847e-05 | 4 | FALSE | FALSE | FALSE | FALSE | 16 | 30 |
| IVNS1ABP | 3.549956e-04 | 2.229496e-04 | -4.345496e-04 | 5 | FALSE | FALSE | FALSE | FALSE | 17 | 19 |
| SLC1A5 | 3.495283e-04 | 1.844679e-04 | 1.458682e-05 | 5 | FALSE | FALSE | FALSE | FALSE | 18 | 38 |
| FAR1 | 3.422899e-04 | 9.989324e-05 | -8.674871e-05 | 5 | FALSE | FALSE | FALSE | FALSE | 19 | 29 |
| WWTR1 | 3.397314e-04 | 1.384696e-04 | -4.099336e-05 | 6 | FALSE | FALSE | FALSE | FALSE | 20 | 32 |
| NDC80 | 3.321227e-04 | 1.593288e-04 | 3.184734e-04 | 6 | FALSE | FALSE | FALSE | FALSE | 21 | 22 |
| CTSB | 3.319458e-04 | 1.892530e-04 | -1.008085e-05 | 4 | FALSE | FALSE | FALSE | FALSE | 22 | 39 |
| KLF4 | 3.295498e-04 | 9.477489e-05 | -1.281867e-04 | 10 | TRUE | FALSE | FALSE | FALSE | 23 | 26 |
| PLK1 | 2.943991e-04 | 1.814412e-04 | 5.684625e-04 | 4 | FALSE | FALSE | FALSE | FALSE | 24 | 13 |
| NFIA | 2.905949e-04 | 1.358932e-04 | 4.548551e-04 | 6 | TRUE | FALSE | FALSE | FALSE | 25 | 17 |
| PTEN | 2.753436e-04 | 2.635258e-04 | -8.389234e-06 | 6 | FALSE | FALSE | FALSE | FALSE | 26 | 41 |
| SLC38A2 | 2.667867e-04 | 2.679345e-04 | -3.606024e-05 | 3 | FALSE | FALSE | FALSE | FALSE | 27 | 34 |
| TNIK | 2.456670e-04 | 1.031096e-04 | -7.062921e-04 | 5 | FALSE | FALSE | FALSE | FALSE | 28 | 7 |
| KIF20B | 2.255165e-04 | 5.346994e-05 | 7.188357e-04 | 2 | FALSE | FALSE | FALSE | FALSE | 29 | 5 |
| MDM2 | 2.236497e-04 | 5.577028e-05 | 5.878168e-04 | 5 | FALSE | FALSE | FALSE | FALSE | 30 | 12 |
| GATA2 | 2.138830e-04 | 6.709211e-05 | -3.728669e-05 | 7 | TRUE | FALSE | FALSE | FALSE | 31 | 33 |
| TCF7L2 | 2.135133e-04 | 5.082929e-05 | 2.495285e-04 | 7 | TRUE | FALSE | FALSE | FALSE | 32 | 25 |
| PLEKHA5 | 2.059156e-04 | 4.903499e-05 | -9.970697e-06 | 3 | FALSE | FALSE | FALSE | FALSE | 33 | 40 |
| SPRY4 | 1.766548e-04 | 0.000000e+00 | 6.104017e-04 | 1 | FALSE | FALSE | FALSE | FALSE | 34 | 10 |
| THRA | 1.487272e-04 | 1.311744e-04 | -4.438621e-05 | 3 | TRUE | FALSE | FALSE | FALSE | 35 | 31 |
| PRDM5 | 1.449123e-04 | 0.000000e+00 | 2.015269e-05 | 1 | TRUE | FALSE | FALSE | FALSE | 36 | 35 |
| ORMDL3 | 1.250970e-04 | 1.241226e-04 | -1.564841e-05 | 2 | FALSE | FALSE | FALSE | FALSE | 37 | 37 |
| KLF10 | 1.225750e-04 | 1.520613e-04 | -3.630311e-04 | 2 | TRUE | FALSE | FALSE | FALSE | 38 | 21 |
| LMO2 | 1.053465e-04 | 8.979617e-05 | -7.769726e-06 | 3 | FALSE | FALSE | FALSE | FALSE | 39 | 42 |
| MYLK | 1.041125e-04 | 1.079196e-05 | 1.929550e-05 | 2 | FALSE | FALSE | FALSE | FALSE | 40 | 36 |
| CCNT2 | 1.026096e-04 | 7.186726e-05 | 5.367263e-06 | 2 | FALSE | FALSE | FALSE | FALSE | 41 | 43 |
| HOXB5 | 9.432974e-05 | 0.000000e+00 | -2.272554e-09 | 1 | TRUE | FALSE | FALSE | FALSE | 42 | 46 |
| CHD7 | 8.325684e-05 | 4.784245e-05 | 2.934812e-04 | 3 | FALSE | FALSE | FALSE | FALSE | 43 | 23 |
| ETS2 | 8.007617e-05 | 2.313154e-05 | -1.115904e-04 | 2 | TRUE | FALSE | FALSE | FALSE | 44 | 27 |
| MAP2K6 | 6.597339e-05 | 0.000000e+00 | 9.907152e-08 | 1 | FALSE | FALSE | FALSE | FALSE | 45 | 44 |
| PLD1 | 4.533708e-05 | 0.000000e+00 | 3.604083e-09 | 1 | FALSE | FALSE | FALSE | FALSE | 46 | 45 |
In [68]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [69]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [70]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [71]:
plot_df
| Gene | variable | value |
|---|---|---|
| <chr> | <chr> | <dbl> |
| AXIN2 | run0 | NA |
| CCND1 | run0 | 6.757519e-04 |
| CCNT2 | run0 | NA |
| CDKN1A | run0 | 6.160467e-04 |
| CEBPD | run0 | 7.550301e-04 |
| CHD7 | run0 | 2.850595e-04 |
| CIITA | run0 | 7.475761e-05 |
| COL4A2 | run0 | 2.771913e-05 |
| CTSB | run0 | 9.563458e-04 |
| CTSK | run0 | NA |
| E2F1 | run0 | 6.434934e-04 |
| ETS2 | run0 | 9.410978e-05 |
| FAR1 | run0 | NA |
| FGFR2 | run0 | NA |
| FOS | run0 | 4.278489e-04 |
| GATA2 | run0 | 1.472863e-04 |
| GJB1 | run0 | 2.190515e-06 |
| GLDC | run0 | 2.539224e-06 |
| HMOX1 | run0 | 7.792052e-04 |
| HNF4A | run0 | 1.452764e-05 |
| HOXB5 | run0 | NA |
| IGFBP3 | run0 | 2.464074e-03 |
| IGFBP7 | run0 | NA |
| IKZF2 | run0 | NA |
| IVNS1ABP | run0 | NA |
| JUNB | run0 | 1.247005e-03 |
| KIF20B | run0 | NA |
| KLF10 | run0 | NA |
| KLF2 | run0 | NA |
| KLF4 | run0 | 2.538671e-04 |
| ⋮ | ⋮ | ⋮ |
| NCOA2 | run9 | 2.135522e-04 |
| NDC80 | run9 | 4.875279e-04 |
| NDRG1 | run9 | 1.536830e-03 |
| NFIA | run9 | 4.054250e-04 |
| ORMDL3 | run9 | NA |
| PIP5K1B | run9 | NA |
| PLA2G4A | run9 | NA |
| PLD1 | run9 | 1.339342e-04 |
| PLEKHA5 | run9 | 6.623127e-04 |
| PLK1 | run9 | 3.379647e-04 |
| PRDM5 | run9 | 2.193218e-05 |
| PTEN | run9 | 2.891620e-04 |
| SBF2 | run9 | NA |
| SESN1 | run9 | NA |
| SIAH1 | run9 | NA |
| SLC1A5 | run9 | 8.802202e-04 |
| SLC38A2 | run9 | 6.103450e-04 |
| SOX5 | run9 | 1.068104e-05 |
| SPRY4 | run9 | NA |
| SVIP | run9 | 1.850812e-03 |
| TANK | run9 | 6.539742e-04 |
| TCF7L2 | run9 | 3.776996e-04 |
| TGM2 | run9 | 7.247475e-04 |
| THRA | run9 | 9.682434e-05 |
| TLE4 | run9 | NA |
| TNIK | run9 | 2.857012e-04 |
| TRPS1 | run9 | NA |
| TTC30A | run9 | NA |
| TTC30B | run9 | NA |
| WWTR1 | run9 | 4.324673e-04 |
In [880]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [881]:
tmp <- plot_df[plot_df$Gene %in% gl,]
tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [882]:
gl
- 'KRT8'
- 'IGFBP3'
- 'SVIP'
- 'MCM7'
- 'JUNB'
- 'LMO7'
- 'TGM2'
- 'NDRG1'
- 'FOS'
- 'TANK'
In [883]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
legend.position = 'none',
panel.grid = element_blank()
)
ridge_p
Picking joint bandwidth of 0.00019
Warning message:
“Removed 2 rows containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [884]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_ridge_p.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_ridge_p.png',
width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.00019 Warning message: “Removed 2 rows containing non-finite outside the scale range (`stat_density_ridges()`).” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Picking joint bandwidth of 0.00019 Warning message: “Removed 2 rows containing non-finite outside the scale range (`stat_density_ridges()`).” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.”
In [72]:
gene <- bitr(drivers$V1, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
'select()' returned 1:1 mapping between keys and columns
In [73]:
gs <- drivers[order(weight_shap_total_mean,decreasing = T),]
ags <- merge(gene,gs,by.x = 'SYMBOL',by.y='V1',all=F)
gs <- ags[order(ags$weight_shap_total_mean,decreasing = T),]
gs <- gs$weight_shap_total_mean
names(gs) <- ags$ENTREZID
In [78]:
em <- GSEA(gs*1000,
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
scoreType = "pos"
#qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
head(em)
preparing geneSet collections... GSEA analysis... leading edge analysis... done...
| ID | Description | setSize | enrichmentScore | NES | pvalue | p.adjust | qvalue | rank | leading_edge | core_enrichment | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <int> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| HALLMARK_NOTCH_SIGNALING | HALLMARK_NOTCH_SIGNALING | HALLMARK_NOTCH_SIGNALING | 2 | 0.9685105 | 1.878309 | 0.005391236 | 0.1940845 | 0.1940845 | 1 | tags=50%, list=2%, signal=51% | 595 |
| HALLMARK_INFLAMMATORY_RESPONSE | HALLMARK_INFLAMMATORY_RESPONSE | HALLMARK_INFLAMMATORY_RESPONSE | 1 | 0.9555556 | 1.938509 | 0.064935065 | 0.4435564 | 0.4435564 | 3 | tags=100%, list=7%, signal=96% | 1026 |
| HALLMARK_INTERFERON_GAMMA_RESPONSE | HALLMARK_INTERFERON_GAMMA_RESPONSE | HALLMARK_INTERFERON_GAMMA_RESPONSE | 1 | 0.9555556 | 1.938509 | 0.064935065 | 0.4435564 | 0.4435564 | 3 | tags=100%, list=7%, signal=96% | 1026 |
| HALLMARK_APOPTOSIS | HALLMARK_APOPTOSIS | HALLMARK_APOPTOSIS | 3 | 0.8430972 | 1.615284 | 0.061938062 | 0.4435564 | 0.4435564 | 3 | tags=67%, list=7%, signal=67% | 595/1026 |
| HALLMARK_ANDROGEN_RESPONSE | HALLMARK_ANDROGEN_RESPONSE | HALLMARK_ANDROGEN_RESPONSE | 4 | 0.8038439 | 1.555136 | 0.073926074 | 0.4435564 | 0.4435564 | 1 | tags=25%, list=2%, signal=27% | 595 |
| HALLMARK_TNFA_SIGNALING_VIA_NFKB | HALLMARK_TNFA_SIGNALING_VIA_NFKB | HALLMARK_TNFA_SIGNALING_VIA_NFKB | 9 | 0.7326978 | 1.464995 | 0.051948052 | 0.4435564 | 0.4435564 | 10 | tags=56%, list=22%, signal=54% | 595/1026/1052/2114/2353 |
In [79]:
em@result
| ID | Description | setSize | enrichmentScore | NES | pvalue | p.adjust | qvalue | rank | leading_edge | core_enrichment | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <int> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | |
| HALLMARK_NOTCH_SIGNALING | HALLMARK_NOTCH_SIGNALING | HALLMARK_NOTCH_SIGNALING | 2 | 0.96851048 | 1.8783090 | 0.005391236 | 0.1940845 | 0.1940845 | 1 | tags=50%, list=2%, signal=51% | 595 |
| HALLMARK_INFLAMMATORY_RESPONSE | HALLMARK_INFLAMMATORY_RESPONSE | HALLMARK_INFLAMMATORY_RESPONSE | 1 | 0.95555556 | 1.9385087 | 0.064935065 | 0.4435564 | 0.4435564 | 3 | tags=100%, list=7%, signal=96% | 1026 |
| HALLMARK_INTERFERON_GAMMA_RESPONSE | HALLMARK_INTERFERON_GAMMA_RESPONSE | HALLMARK_INTERFERON_GAMMA_RESPONSE | 1 | 0.95555556 | 1.9385087 | 0.064935065 | 0.4435564 | 0.4435564 | 3 | tags=100%, list=7%, signal=96% | 1026 |
| HALLMARK_APOPTOSIS | HALLMARK_APOPTOSIS | HALLMARK_APOPTOSIS | 3 | 0.84309718 | 1.6152845 | 0.061938062 | 0.4435564 | 0.4435564 | 3 | tags=67%, list=7%, signal=67% | 595/1026 |
| HALLMARK_ANDROGEN_RESPONSE | HALLMARK_ANDROGEN_RESPONSE | HALLMARK_ANDROGEN_RESPONSE | 4 | 0.80384390 | 1.5551363 | 0.073926074 | 0.4435564 | 0.4435564 | 1 | tags=25%, list=2%, signal=27% | 595 |
| HALLMARK_TNFA_SIGNALING_VIA_NFKB | HALLMARK_TNFA_SIGNALING_VIA_NFKB | HALLMARK_TNFA_SIGNALING_VIA_NFKB | 9 | 0.73269780 | 1.4649955 | 0.051948052 | 0.4435564 | 0.4435564 | 10 | tags=56%, list=22%, signal=54% | 595/1026/1052/2114/2353 |
| HALLMARK_COAGULATION | HALLMARK_COAGULATION | HALLMARK_COAGULATION | 1 | 0.88888889 | 1.8032639 | 0.134865135 | 0.5334665 | 0.5334665 | 6 | tags=100%, list=13%, signal=89% | 1508 |
| HALLMARK_COMPLEMENT | HALLMARK_COMPLEMENT | HALLMARK_COMPLEMENT | 1 | 0.88888889 | 1.8032639 | 0.134865135 | 0.5334665 | 0.5334665 | 6 | tags=100%, list=13%, signal=89% | 1508 |
| HALLMARK_HEDGEHOG_SIGNALING | HALLMARK_HEDGEHOG_SIGNALING | HALLMARK_HEDGEHOG_SIGNALING | 1 | 0.84444444 | 1.7131007 | 0.177822178 | 0.5334665 | 0.5334665 | 8 | tags=100%, list=17%, signal=84% | 2114 |
| HALLMARK_IL6_JAK_STAT3_SIGNALING | HALLMARK_IL6_JAK_STAT3_SIGNALING | HALLMARK_IL6_JAK_STAT3_SIGNALING | 1 | 0.75555556 | 1.5327743 | 0.245754246 | 0.5334665 | 0.5334665 | 12 | tags=100%, list=26%, signal=76% | 3162 |
| HALLMARK_MTORC1_SIGNALING | HALLMARK_MTORC1_SIGNALING | HALLMARK_MTORC1_SIGNALING | 3 | 0.78252132 | 1.4992276 | 0.133866134 | 0.5334665 | 0.5334665 | 3 | tags=33%, list=7%, signal=33% | 1026 |
| HALLMARK_PI3K_AKT_MTOR_SIGNALING | HALLMARK_PI3K_AKT_MTOR_SIGNALING | HALLMARK_PI3K_AKT_MTOR_SIGNALING | 4 | 0.72939860 | 1.4111126 | 0.172827173 | 0.5334665 | 0.5334665 | 7 | tags=50%, list=15%, signal=46% | 1026/1869 |
| HALLMARK_ESTROGEN_RESPONSE_LATE | HALLMARK_ESTROGEN_RESPONSE_LATE | HALLMARK_ESTROGEN_RESPONSE_LATE | 3 | 0.72982844 | 1.3982736 | 0.219780220 | 0.5334665 | 0.5334665 | 10 | tags=67%, list=22%, signal=56% | 595/2353 |
| HALLMARK_G2M_CHECKPOINT | HALLMARK_G2M_CHECKPOINT | HALLMARK_G2M_CHECKPOINT | 5 | 0.70566198 | 1.3750083 | 0.156843157 | 0.5334665 | 0.5334665 | 7 | tags=40%, list=15%, signal=38% | 595/1869 |
| HALLMARK_E2F_TARGETS | HALLMARK_E2F_TARGETS | HALLMARK_E2F_TARGETS | 3 | 0.71585675 | 1.3715053 | 0.232767233 | 0.5334665 | 0.5334665 | 3 | tags=33%, list=7%, signal=33% | 1026 |
| HALLMARK_UV_RESPONSE_UP | HALLMARK_UV_RESPONSE_UP | HALLMARK_UV_RESPONSE_UP | 3 | 0.69767442 | 1.3366699 | 0.262737263 | 0.5334665 | 0.5334665 | 16 | tags=100%, list=35%, signal=70% | 2353/3162/3726 |
| HALLMARK_XENOBIOTIC_METABOLISM | HALLMARK_XENOBIOTIC_METABOLISM | HALLMARK_XENOBIOTIC_METABOLISM | 3 | 0.69500573 | 1.3315570 | 0.266733267 | 0.5334665 | 0.5334665 | 12 | tags=67%, list=26%, signal=53% | 2114/3162 |
| HALLMARK_HYPOXIA | HALLMARK_HYPOXIA | HALLMARK_HYPOXIA | 6 | 0.66875977 | 1.3198248 | 0.196803197 | 0.5334665 | 0.5334665 | 14 | tags=67%, list=30%, signal=53% | 1026/2353/3162/3486 |
| HALLMARK_GLYCOLYSIS | HALLMARK_GLYCOLYSIS | HALLMARK_GLYCOLYSIS | 1 | 0.71111111 | 1.4426111 | 0.293706294 | 0.5343228 | 0.5343228 | 14 | tags=100%, list=30%, signal=71% | 3486 |
| HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY | HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY | HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY | 1 | 0.66666667 | 1.3524479 | 0.311688312 | 0.5343228 | 0.5343228 | 16 | tags=100%, list=35%, signal=67% | 3726 |
| HALLMARK_ESTROGEN_RESPONSE_EARLY | HALLMARK_ESTROGEN_RESPONSE_EARLY | HALLMARK_ESTROGEN_RESPONSE_EARLY | 6 | 0.60780293 | 1.1995240 | 0.308691309 | 0.5343228 | 0.5343228 | 20 | tags=83%, list=43%, signal=54% | 595/2353/7071/9314/3856 |
| HALLMARK_HEME_METABOLISM | HALLMARK_HEME_METABOLISM | HALLMARK_HEME_METABOLISM | 2 | 0.66179246 | 1.2834665 | 0.336663337 | 0.5509036 | 0.5509036 | 6 | tags=50%, list=13%, signal=45% | 1508 |
| HALLMARK_KRAS_SIGNALING_UP | HALLMARK_KRAS_SIGNALING_UP | HALLMARK_KRAS_SIGNALING_UP | 2 | 0.61363636 | 1.1900736 | 0.413586414 | 0.5947898 | 0.5947898 | 19 | tags=100%, list=41%, signal=61% | 3486/9314 |
| HALLMARK_MYOGENESIS | HALLMARK_MYOGENESIS | HALLMARK_MYOGENESIS | 4 | 0.58930384 | 1.1400818 | 0.399600400 | 0.5947898 | 0.5947898 | 3 | tags=25%, list=7%, signal=26% | 1026 |
| HALLMARK_PANCREAS_BETA_CELLS | HALLMARK_PANCREAS_BETA_CELLS | HALLMARK_PANCREAS_BETA_CELLS | 1 | 0.55555556 | 1.1270399 | 0.429570430 | 0.5947898 | 0.5947898 | 21 | tags=100%, list=46%, signal=56% | 4005 |
| HALLMARK_P53_PATHWAY | HALLMARK_P53_PATHWAY | HALLMARK_P53_PATHWAY | 6 | 0.55558695 | 1.0964736 | 0.424575425 | 0.5947898 | 0.5947898 | 12 | tags=50%, list=26%, signal=42% | 1026/2353/3162 |
| HALLMARK_TGF_BETA_SIGNALING | HALLMARK_TGF_BETA_SIGNALING | HALLMARK_TGF_BETA_SIGNALING | 3 | 0.56916757 | 1.0904645 | 0.468531469 | 0.6178108 | 0.6178108 | 18 | tags=67%, list=39%, signal=43% | 3726/7071 |
| HALLMARK_MYC_TARGETS_V1 | HALLMARK_MYC_TARGETS_V1 | HALLMARK_MYC_TARGETS_V1 | 1 | 0.48888889 | 0.9917951 | 0.480519481 | 0.6178108 | 0.6178108 | 24 | tags=100%, list=52%, signal=49% | |
| HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION | HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION | HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION | 4 | 0.38563173 | 0.7460527 | 0.733266733 | 0.8350400 | 0.8350400 | 26 | tags=75%, list=57%, signal=36% | 3486/4176/4638 |
| HALLMARK_MYC_TARGETS_V2 | HALLMARK_MYC_TARGETS_V2 | HALLMARK_MYC_TARGETS_V2 | 1 | 0.28888889 | 0.5860608 | 0.713286713 | 0.8350400 | 0.8350400 | 15 | tags=100%, list=33%, signal=69% | |
| HALLMARK_APICAL_JUNCTION | HALLMARK_APICAL_JUNCTION | HALLMARK_APICAL_JUNCTION | 1 | 0.24444444 | 0.4958976 | 0.742257742 | 0.8350400 | 0.8350400 | 13 | tags=100%, list=28%, signal=73% | |
| HALLMARK_UV_RESPONSE_DN | HALLMARK_UV_RESPONSE_DN | HALLMARK_UV_RESPONSE_DN | 1 | 0.24444444 | 0.4958976 | 0.742257742 | 0.8350400 | 0.8350400 | 13 | tags=100%, list=28%, signal=73% | |
| HALLMARK_MITOTIC_SPINDLE | HALLMARK_MITOTIC_SPINDLE | HALLMARK_MITOTIC_SPINDLE | 3 | 0.30232558 | 0.5792236 | 0.802197802 | 0.8493859 | 0.8493859 | 31 | tags=100%, list=67%, signal=35% | 10403/5347 |
| HALLMARK_PEROXISOME | HALLMARK_PEROXISOME | HALLMARK_PEROXISOME | 1 | 0.22222222 | 0.4508160 | 0.781218781 | 0.8493859 | 0.8493859 | 12 | tags=100%, list=26%, signal=76% | |
| HALLMARK_ADIPOGENESIS | HALLMARK_ADIPOGENESIS | HALLMARK_ADIPOGENESIS | 3 | 0.11627907 | 0.2227783 | 0.949050949 | 0.9761667 | 0.9761667 | 22 | tags=100%, list=48%, signal=56% | 6510/10010 |
| HALLMARK_IL2_STAT5_SIGNALING | HALLMARK_IL2_STAT5_SIGNALING | HALLMARK_IL2_STAT5_SIGNALING | 4 | 0.07142857 | 0.1381875 | 0.982017982 | 0.9820180 | 0.9820180 | 20 | tags=100%, list=43%, signal=62% | 6510/81848/7052 |
In [81]:
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
head(em)
| ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| HALLMARK_TNFA_SIGNALING_VIA_NFKB | HALLMARK_TNFA_SIGNALING_VIA_NFKB | HALLMARK_TNFA_SIGNALING_VIA_NFKB | 9/31 | 200/4383 | 6.024759e-06 | 0.0002168913 | 0.0001648881 | 595/1026/1052/2114/2353/3726/7071/9314/10010 | 9 |
| HALLMARK_ESTROGEN_RESPONSE_EARLY | HALLMARK_ESTROGEN_RESPONSE_EARLY | HALLMARK_ESTROGEN_RESPONSE_EARLY | 6/31 | 200/4383 | 2.366885e-03 | 0.0213019655 | 0.0161944767 | 595/2353/7071/9314/3856/7052 | 6 |
| HALLMARK_HYPOXIA | HALLMARK_HYPOXIA | HALLMARK_HYPOXIA | 6/31 | 200/4383 | 2.366885e-03 | 0.0213019655 | 0.0161944767 | 1026/2353/3162/3486/10397/7052 | 6 |
| HALLMARK_P53_PATHWAY | HALLMARK_P53_PATHWAY | HALLMARK_P53_PATHWAY | 6/31 | 200/4383 | 2.366885e-03 | 0.0213019655 | 0.0161944767 | 1026/2353/3162/9314/4193/10397 | 6 |
| HALLMARK_ANDROGEN_RESPONSE | HALLMARK_ANDROGEN_RESPONSE | HALLMARK_ANDROGEN_RESPONSE | 4/31 | 100/4383 | 5.001278e-03 | 0.0320284596 | 0.0243491213 | 595/3856/10397/54407 | 4 |
| HALLMARK_PI3K_AKT_MTOR_SIGNALING | HALLMARK_PI3K_AKT_MTOR_SIGNALING | HALLMARK_PI3K_AKT_MTOR_SIGNALING | 4/31 | 105/4383 | 5.948489e-03 | 0.0320284596 | 0.0243491213 | 1026/1869/5608/5728 | 4 |
In [96]:
em@result
| ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | |
|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <chr> | <int> | |
| Tnfa Signaling Via Nfkb | Tnfa Signaling Via Nfkb | Tnfa Signaling Via Nfkb | 9/31 | 200/4383 | 6.024759e-06 | 0.0002168913 | 0.0001648881 | 595/1026/1052/2114/2353/3726/7071/9314/10010 | 9 |
| Estrogen Response Early | Estrogen Response Early | Estrogen Response Early | 6/31 | 200/4383 | 2.366885e-03 | 0.0213019655 | 0.0161944767 | 595/2353/7071/9314/3856/7052 | 6 |
| Hypoxia | Hypoxia | Hypoxia | 6/31 | 200/4383 | 2.366885e-03 | 0.0213019655 | 0.0161944767 | 1026/2353/3162/3486/10397/7052 | 6 |
| P53 Pathway | P53 Pathway | P53 Pathway | 6/31 | 200/4383 | 2.366885e-03 | 0.0213019655 | 0.0161944767 | 1026/2353/3162/9314/4193/10397 | 6 |
| Androgen Response | Androgen Response | Androgen Response | 4/31 | 100/4383 | 5.001278e-03 | 0.0320284596 | 0.0243491213 | 595/3856/10397/54407 | 4 |
| Pi3k Akt Mtor Signaling | Pi3k Akt Mtor Signaling | Pi3k Akt Mtor Signaling | 4/31 | 105/4383 | 5.948489e-03 | 0.0320284596 | 0.0243491213 | 1026/1869/5608/5728 | 4 |
| Tgf Beta Signaling | Tgf Beta Signaling | Tgf Beta Signaling | 3/31 | 54/4383 | 6.227756e-03 | 0.0320284596 | 0.0243491213 | 3726/7071/25937 | 3 |
| G2m Checkpoint | G2m Checkpoint | G2m Checkpoint | 5/31 | 200/4383 | 1.213097e-02 | 0.0545893869 | 0.0415007035 | 595/1869/9585/10403/5347 | 5 |
| Notch Signaling | Notch Signaling | Notch Signaling | 2/31 | 32/4383 | 2.104869e-02 | 0.0841947743 | 0.0640077232 | 595/6934 | 2 |
| Il2 Stat5 Signaling | Il2 Stat5 Signaling | Il2 Stat5 Signaling | 4/31 | 199/4383 | 4.971368e-02 | 0.1514420214 | 0.1151313613 | 10397/6510/81848/7052 | 4 |
| Epithelial Mesenchymal Transition | Epithelial Mesenchymal Transition | Epithelial Mesenchymal Transition | 4/31 | 200/4383 | 5.048067e-02 | 0.1514420214 | 0.1151313613 | 3486/4176/4638/7052 | 4 |
| Myogenesis | Myogenesis | Myogenesis | 4/31 | 200/4383 | 5.048067e-02 | 0.1514420214 | 0.1151313613 | 1026/3486/4638/25937 | 4 |
| Uv Response Up | Uv Response Up | Uv Response Up | 3/31 | 158/4383 | 9.910482e-02 | 0.2659811773 | 0.2022079126 | 2353/3162/3726 | 3 |
| Apoptosis | Apoptosis | Apoptosis | 3/31 | 161/4383 | 1.034371e-01 | 0.2659811773 | 0.2022079126 | 595/1026/3162 | 3 |
| Mitotic Spindle | Mitotic Spindle | Mitotic Spindle | 3/31 | 199/4383 | 1.643245e-01 | 0.2989046819 | 0.2272374775 | 9585/10403/5347 | 3 |
| Adipogenesis | Adipogenesis | Adipogenesis | 3/31 | 200/4383 | 1.660582e-01 | 0.2989046819 | 0.2272374775 | 4638/6510/10010 | 3 |
| E2f Targets | E2f Targets | E2f Targets | 3/31 | 200/4383 | 1.660582e-01 | 0.2989046819 | 0.2272374775 | 1026/4176/5347 | 3 |
| Estrogen Response Late | Estrogen Response Late | Estrogen Response Late | 3/31 | 200/4383 | 1.660582e-01 | 0.2989046819 | 0.2272374775 | 595/2353/9314 | 3 |
| Mtorc1 Signaling | Mtorc1 Signaling | Mtorc1 Signaling | 3/31 | 200/4383 | 1.660582e-01 | 0.2989046819 | 0.2272374775 | 1026/5347/6510 | 3 |
| Xenobiotic Metabolism | Xenobiotic Metabolism | Xenobiotic Metabolism | 3/31 | 200/4383 | 1.660582e-01 | 0.2989046819 | 0.2272374775 | 2114/3162/6510 | 3 |
| Hedgehog Signaling | Hedgehog Signaling | Hedgehog Signaling | 1/31 | 36/4383 | 2.262870e-01 | 0.3879205687 | 0.2949103738 | 2114 | 1 |
| Pancreas Beta Cells | Pancreas Beta Cells | Pancreas Beta Cells | 1/31 | 40/4383 | 2.481300e-01 | 0.4060308702 | 0.3086784394 | 4005 | 1 |
| Reactive Oxygen Species Pathway | Reactive Oxygen Species Pathway | Reactive Oxygen Species Pathway | 1/31 | 49/4383 | 2.951168e-01 | 0.4619219454 | 0.3511687304 | 3726 | 1 |
| Myc Targets V2 | Myc Targets V2 | Myc Targets V2 | 1/31 | 58/4383 | 3.392562e-01 | 0.5088843161 | 0.3868711175 | 5347 | 1 |
| Heme Metabolism | Heme Metabolism | Heme Metabolism | 2/31 | 200/4383 | 4.169591e-01 | 0.5773280103 | 0.4389043353 | 1508/4005 | 2 |
| Kras Signaling Up | Kras Signaling Up | Kras Signaling Up | 2/31 | 200/4383 | 4.169591e-01 | 0.5773280103 | 0.4389043353 | 3486/9314 | 2 |
| Il6 Jak Stat3 Signaling | Il6 Jak Stat3 Signaling | Il6 Jak Stat3 Signaling | 1/31 | 87/4383 | 4.640314e-01 | 0.6187084736 | 0.4703631670 | 3162 | 1 |
| Peroxisome | Peroxisome | Peroxisome | 1/31 | 104/4383 | 5.262272e-01 | 0.6765777743 | 0.5143573723 | 6477 | 1 |
| Coagulation | Coagulation | Coagulation | 1/31 | 138/4383 | 6.303523e-01 | 0.7661191171 | 0.5824297381 | 1508 | 1 |
| Uv Response Dn | Uv Response Dn | Uv Response Dn | 1/31 | 144/4383 | 6.462653e-01 | 0.7661191171 | 0.5824297381 | 5728 | 1 |
| Apical Junction | Apical Junction | Apical Junction | 1/31 | 200/4383 | 7.661191e-01 | 0.7661191171 | 0.5824297381 | 5728 | 1 |
| Complement | Complement | Complement | 1/31 | 200/4383 | 7.661191e-01 | 0.7661191171 | 0.5824297381 | 1508 | 1 |
| Glycolysis | Glycolysis | Glycolysis | 1/31 | 200/4383 | 7.661191e-01 | 0.7661191171 | 0.5824297381 | 3486 | 1 |
| Inflammatory Response | Inflammatory Response | Inflammatory Response | 1/31 | 200/4383 | 7.661191e-01 | 0.7661191171 | 0.5824297381 | 1026 | 1 |
| Interferon Gamma Response | Interferon Gamma Response | Interferon Gamma Response | 1/31 | 200/4383 | 7.661191e-01 | 0.7661191171 | 0.5824297381 | 1026 | 1 |
| Myc Targets V1 | Myc Targets V1 | Myc Targets V1 | 1/31 | 200/4383 | 7.661191e-01 | 0.7661191171 | 0.5824297381 | 4176 | 1 |
In [95]:
m_t2g <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, entrez_gene)
m_t2g$gs_name <- m_t2g$gs_name %>%
stringr::str_replace_all(pattern = 'HALLMARK_',replacement = '') %>%
stringr::str_replace_all(pattern = '_',replacement = ' ') %>%
stringr::str_to_title()
kk <- enrichKEGG(gene = names(gs),
#keyType = 'uniprot',
organism = 'hsa',
pvalueCutoff = 0.05)
#head(kk)
em <- enricher(names(gs),
TERM2GENE=m_t2g,
minGSSize = 0,
#maxGSSize = 500,
pvalueCutoff = 1,
#scoreType = "pos"
qvalueCutoff = 1,
#eps = eps,
#verbose = FALSE
)
In [109]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- em@result$Description
high_mean_ls <-c()
for (i in 1:nrow(em@result)){
if (i==1){
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- em@geneSets[em@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
length(table(high_plot_df$gs))
table(high_plot_df$gs)
36
Adipogenesis Androgen Response
3 4
Apical Junction Apoptosis
1 3
Coagulation Complement
1 1
E2f Targets Epithelial Mesenchymal Transition
3 4
Estrogen Response Early Estrogen Response Late
6 3
G2m Checkpoint Glycolysis
5 1
Hedgehog Signaling Heme Metabolism
1 2
Hypoxia Il2 Stat5 Signaling
6 4
Il6 Jak Stat3 Signaling Inflammatory Response
1 1
Interferon Gamma Response Kras Signaling Up
1 2
Mitotic Spindle Mtorc1 Signaling
3 3
Myc Targets V1 Myc Targets V2
1 1
Myogenesis Notch Signaling
4 2
P53 Pathway Pancreas Beta Cells
6 1
Peroxisome Pi3k Akt Mtor Signaling
1 4
Reactive Oxygen Species Pathway Tgf Beta Signaling
1 3
Tnfa Signaling Via Nfkb Uv Response Dn
9 1
Uv Response Up Xenobiotic Metabolism
3 3
In [110]:
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
add
| weight_shap_total_mean | gs | |
|---|---|---|
| <dbl> | <chr> | |
| 110 | 5.943084e-04 | Notch Signaling |
| 42 | 2.135133e-04 | Notch Signaling |
| 82 | 8.007617e-05 | Hedgehog Signaling |
| 21 | 1.053465e-04 | Pancreas Beta Cells |
| 163 | 1.167002e-03 | Reactive Oxygen Species Pathway |
| 335 | 2.943991e-04 | Myc Targets V2 |
| 6 | 3.319458e-04 | Heme Metabolism |
| 211 | 1.053465e-04 | Heme Metabolism |
| 143 | 1.767575e-03 | Kras Signaling Up |
| 194 | 3.295498e-04 | Kras Signaling Up |
| 125 | 6.298396e-04 | Il6 Jak Stat3 Signaling |
| 361 | 3.769688e-04 | Peroxisome |
| 61 | 3.319458e-04 | Coagulation |
| 351 | 2.753436e-04 | Uv Response Dn |
| 352 | 2.753436e-04 | Apical Junction |
| 62 | 3.319458e-04 | Complement |
| 144 | 1.767575e-03 | Glycolysis |
| 312 | 5.431046e-04 | Inflammatory Response |
| 313 | 5.431046e-04 | Interferon Gamma Response |
| 242 | 1.453233e-03 | Myc Targets V1 |
In [111]:
high_plot_df <- rbind(high_plot_df,add,add)
length(table(high_plot_df$gs))
table(high_plot_df$gs)
36
Adipogenesis Androgen Response
3 4
Apical Junction Apoptosis
3 3
Coagulation Complement
3 3
E2f Targets Epithelial Mesenchymal Transition
3 4
Estrogen Response Early Estrogen Response Late
6 3
G2m Checkpoint Glycolysis
5 3
Hedgehog Signaling Heme Metabolism
3 6
Hypoxia Il2 Stat5 Signaling
6 4
Il6 Jak Stat3 Signaling Inflammatory Response
3 3
Interferon Gamma Response Kras Signaling Up
3 6
Mitotic Spindle Mtorc1 Signaling
3 3
Myc Targets V1 Myc Targets V2
3 3
Myogenesis Notch Signaling
4 6
P53 Pathway Pancreas Beta Cells
6 3
Peroxisome Pi3k Akt Mtor Signaling
3 4
Reactive Oxygen Species Pathway Tgf Beta Signaling
3 3
Tnfa Signaling Via Nfkb Uv Response Dn
9 3
Uv Response Up Xenobiotic Metabolism
3 3
In [112]:
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
head(high_plot_df)
| gs | weight_shap_total_mean | mean | |
|---|---|---|---|
| <chr> | <dbl> | <dbl> | |
| 1 | Adipogenesis | 0.0001041125 | 0.0004263041 |
| 2 | Adipogenesis | 0.0008252715 | 0.0004263041 |
| 3 | Adipogenesis | 0.0003495283 | 0.0004263041 |
| 4 | Androgen Response | 0.0029012616 | 0.0011672718 |
| 5 | Androgen Response | 0.0009067307 | 0.0011672718 |
| 6 | Androgen Response | 0.0005943084 | 0.0011672718 |
In [113]:
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
In [114]:
colnames(high_plot_df) <- c('gs','Weight','mean')
In [115]:
pRidge_H <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'Hallmark',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_H
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.453
In [334]:
png('weight_pRidge_all_meancolor1.png',height = 500*5,width = 600*5,res=400)
pRidge
dev.off()
Picking joint bandwidth of 0.322
png: 2

In [116]:
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_pRidge_H.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_H,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_pRidge_H.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.453 Picking joint bandwidth of 0.453
In [105]:
high_df <- ags[order(ags$weight_shap_total_mean,decreasing = T),] %>% dplyr::select(c('ENTREZID','weight_shap_total_mean'))
gs_name <- kk@result[1:30,]$Description
high_mean_ls <-c()
for (i in 1:nrow(kk@result[1:30,])){
if (i==1){
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
high_plot_df <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_mean_ls <- c(high_mean_ls,mean(high_plot_df$weight_shap_total_mean,na.rm = T))
}else{
ls <- kk@geneSets[kk@result[i,'ID']]
where <- which(high_df$ENTREZID %in% ls[[1]])
tmp <- high_df[where,]
tmp$gs <- gs_name[i]
tmp <- tmp %>% dplyr::select(c('weight_shap_total_mean','gs'))
high_plot_df <- rbind(high_plot_df,tmp)
high_mean_ls <- c(high_mean_ls,mean(tmp$weight_shap_total_mean,,na.rm = T))
}
}
high_mean_df <- data.frame(gs=gs_name,mean=high_mean_ls)
add <- high_plot_df[high_plot_df$gs %in% names(which(table(high_plot_df$gs) == 1 | table(high_plot_df$gs) == 2)),]
high_plot_df <- rbind(high_plot_df,add,add)
high_plot_df <- merge(high_plot_df,high_mean_df,by='gs',all.x=T)
high_mean_df <- high_mean_df[order(high_mean_df$mean,decreasing=T),]
high_plot_df$gs<-factor(high_plot_df$gs,levels=rev(high_mean_df$gs))#排序
colnames(high_plot_df) <- c('gs','Weight','mean')
In [106]:
pRidge_K <- ggplot(high_plot_df,aes(x = scale(Weight), y = gs, fill = log(mean))) +
ggridges::geom_density_ridges_gradient(scale = 3,size=0.6) +
labs(title = 'KEGG',fill = 'Log10 mean of weight') +
xlab('SHAP weight')+
ylab('Gene set')+
scale_y_discrete(labels=function(x){stringr::str_wrap(x, width=30)})+
scale_fill_gradientn(name = "Log10 mean \nof weight",colors=c("#AFD1E6","#C3CFC6",'#E7B46A',"#F6A015"),values=c(0,0.4,0.6,2))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
plot.background = element_rect(fill = "transparent", color = 'white'), # bg of the plot
panel.background = element_rect(fill = "transparent"), # bg of the panel
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_text(face = 'bold',size=18),
legend.position = 'right'
)
pRidge_K
Warning message in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.6): “Ignoring unknown parameters: `size`” Picking joint bandwidth of 0.413
In [107]:
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_pRidge_K.pdf',
width =16/1.5, height =24/1.5)
ggsave(plot = pRidge_K,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/ext_PC9_cyc_pRidge_K.png',
width =16/1.5, height =24/1.5)
Picking joint bandwidth of 0.413 Picking joint bandwidth of 0.413
In [ ]:
In [127]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t14_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
In [128]:
t14_drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(t14_drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| HLA-B | 0.006611837 | 0.0014072035 | -0.003888358 | 10 | FALSE | FALSE | FALSE | FALSE |
| H2AFZ | 0.001543479 | 0.0003817574 | 0.001596191 | 10 | FALSE | TRUE | FALSE | TRUE |
| UBC | 0.003014871 | 0.0006034041 | -0.003552045 | 10 | FALSE | FALSE | FALSE | FALSE |
| IGFBP5 | 0.011728111 | 0.0037999116 | 0.013004079 | 10 | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.009201345 | 0.0016104131 | 0.004694919 | 10 | FALSE | FALSE | FALSE | FALSE |
| SAT1 | 0.003824356 | 0.0007941552 | -0.001987261 | 10 | FALSE | FALSE | FALSE | FALSE |
In [137]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_ncyc_cyc'
read_dir <- file.path(read_dir,run_name)
t0_drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(t0_drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| PSMC4 | 0.0009237856 | 0.0003805723 | 0.003996823 | 10 | FALSE | FALSE | FALSE | FALSE |
| LMO7 | 0.0008511244 | 0.0001933136 | 0.018705904 | 10 | FALSE | FALSE | FALSE | FALSE |
| TGM2 | 0.0008014264 | 0.0002573693 | 0.002679401 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD24 | 0.0013381759 | 0.0002601511 | 0.006296788 | 10 | FALSE | FALSE | FALSE | FALSE |
| TPM1 | 0.0018060397 | 0.0003299156 | 0.008614676 | 10 | FALSE | FALSE | FALSE | FALSE |
| SRSF7 | 0.0012505469 | 0.0003662743 | -0.003673011 | 10 | FALSE | FALSE | FALSE | FALSE |
In [143]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_t14_cyc'
read_dir <- file.path(read_dir,run_name)
t0_14_drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(t0_14_drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| FN1 | 0.007993378 | 0.0007537607 | 1.907695e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| HIST1H2BD | 0.003864827 | 0.0005008594 | 1.813786e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.002306744 | 0.0002728280 | 1.020445e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| KRT8 | 0.004171373 | 0.0006077977 | 7.349687e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| GSTM3 | 0.006570227 | 0.0004776801 | 1.470234e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD9 | 0.004286982 | 0.0004431204 | 1.179431e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
In [138]:
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| KRT8 | 0.0029012616 | 0.0003359210 | 0.0013636484 | 10 | FALSE | FALSE | FALSE | FALSE |
| CCND1 | 0.0005943084 | 0.0002472813 | 0.0002804691 | 10 | FALSE | FALSE | FALSE | FALSE |
| IGFBP3 | 0.0017675749 | 0.0005854681 | -0.0004515210 | 10 | FALSE | FALSE | FALSE | FALSE |
| FOS | 0.0008979096 | 0.0003130372 | -0.0005937169 | 10 | TRUE | FALSE | FALSE | FALSE |
| NDRG1 | 0.0009067307 | 0.0004197639 | -0.0005180585 | 10 | FALSE | FALSE | FALSE | FALSE |
| MCM7 | 0.0014532326 | 0.0003902143 | -0.0007466146 | 10 | FALSE | FALSE | FALSE | FALSE |
In [134]:
intersect(t14_drivers$V1, drivers$V1)
- 'CCND1'
- 'KLF4'
- 'JUNB'
- 'CEBPD'
- 'GATA2'
- 'FOS'
In [133]:
library(ggvenn)
Loading required package: grid
In [144]:
a <- list(T14 = t14_drivers$V1,
EXT = drivers$V1,
T0 = t0_drivers$V1,
'T0-14' = t0_14_drivers$V1)
p1=ggvenn(a, c("T14", "EXT","T0",'T0-14'))
p1
In [145]:
ggsave(plot = p1,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/cyc_venn.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = p1,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/cyc_venn.png',
width =16/1.5, height =16/1.5)
sen¶
SEN¶
load GDSC¶
In [274]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_sen_res'
read_dir <- file.path(read_dir,run_name)
In [275]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| CD24 | 0.0008392735 | 0.0003266406 | -5.656427e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| GTF2B | 0.0010324697 | 0.0003080479 | -1.316672e-04 | 10 | TRUE | FALSE | FALSE | FALSE |
| MSH6 | 0.0016612993 | 0.0004270973 | 7.425508e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| TFDP1 | 0.0013715578 | 0.0005694479 | -5.785423e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| MYO1B | 0.0010195644 | 0.0002886838 | 1.748662e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TUBA1B | 0.0018392079 | 0.0003183509 | -1.412048e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
In [276]:
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
In [277]:
drivers[order(drivers$weight_shap_total_mean,decreasing = T),]
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> |
| SLC1A5 | 0.0021976048 | 6.430204e-04 | 1.082030e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 1 | 8 |
| BARD1 | 0.0021673360 | 6.107930e-04 | 1.084314e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 2 | 7 |
| CD9 | 0.0020897718 | 4.177533e-04 | -1.463574e-04 | 6 | FALSE | FALSE | FALSE | FALSE | 3 | 4 |
| DAAM1 | 0.0020700805 | 4.885557e-04 | 1.041791e-04 | 4 | FALSE | FALSE | FALSE | FALSE | 4 | 10 |
| TUBA1B | 0.0018392079 | 3.183509e-04 | -1.412048e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 5 | 5 |
| MSH6 | 0.0016612993 | 4.270973e-04 | 7.425508e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 6 | 12 |
| TFDP1 | 0.0013715578 | 5.694479e-04 | -5.785423e-05 | 10 | TRUE | FALSE | FALSE | FALSE | 7 | 18 |
| ANXA2 | 0.0013427509 | 6.148865e-04 | -7.150776e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 8 | 14 |
| RAB31 | 0.0011450001 | 3.613637e-04 | -5.959134e-05 | 8 | FALSE | FALSE | FALSE | FALSE | 9 | 16 |
| PLK2 | 0.0011322402 | 5.536364e-04 | 6.720297e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 10 | 15 |
| GTF2B | 0.0010324697 | 3.080479e-04 | -1.316672e-04 | 10 | TRUE | FALSE | FALSE | FALSE | 11 | 6 |
| MYO1B | 0.0010195644 | 2.886838e-04 | 1.748662e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 12 | 3 |
| DGKE | 0.0009730622 | 2.876285e-04 | -5.831443e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 13 | 17 |
| PSMC4 | 0.0009214443 | 3.828258e-04 | -4.451585e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 14 | 20 |
| CD24 | 0.0008392735 | 3.266406e-04 | -5.656427e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 15 | 19 |
| TGM2 | 0.0007415661 | 2.308803e-04 | 5.989809e-06 | 10 | FALSE | FALSE | FALSE | FALSE | 16 | 26 |
| HSPA2 | 0.0006995864 | 2.921626e-04 | -2.506919e-04 | 8 | FALSE | FALSE | FALSE | FALSE | 17 | 1 |
| RRBP1 | 0.0006809303 | 2.972012e-04 | -1.221697e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 18 | 23 |
| TUBB4B | 0.0006375848 | 2.468592e-04 | 1.148352e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 19 | 24 |
| CEBPD | 0.0006121597 | 2.758779e-04 | 7.729752e-05 | 9 | TRUE | FALSE | FALSE | FALSE | 20 | 11 |
| LMO7 | 0.0006043960 | 2.033280e-04 | 2.352714e-04 | 6 | FALSE | FALSE | FALSE | FALSE | 21 | 2 |
| TRIB1 | 0.0004753006 | 2.120645e-04 | 2.118026e-05 | 6 | FALSE | FALSE | FALSE | FALSE | 22 | 22 |
| HIST1H1C | 0.0003803697 | 1.031536e-04 | -7.409843e-05 | 4 | FALSE | FALSE | FALSE | FALSE | 23 | 13 |
| KLF5 | 0.0003624802 | 2.617805e-04 | 1.071805e-04 | 6 | TRUE | FALSE | FALSE | FALSE | 24 | 9 |
| TPM1 | 0.0003337573 | 1.718587e-04 | 2.991494e-05 | 6 | FALSE | FALSE | FALSE | FALSE | 25 | 21 |
| TFAP2C | 0.0002400025 | 8.345076e-05 | 1.130995e-05 | 7 | TRUE | FALSE | FALSE | FALSE | 26 | 25 |
In [1090]:
read_dir
'/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new//main_PC9_LUNG_run10_t0_sen_res'
In [1091]:
tdf <- fread(file.path(read_dir,'driver_info_0.csv')) %>% dplyr::select(c('V1','weight_shap_total'))
In [1092]:
file_name = paste0('driver_info_',0,'.csv')
df <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
for (i in 1:9){
file_name = paste0('driver_info_',i,'.csv')
tdf <- fread(file.path(read_dir,file_name)) %>% dplyr::select(c('V1','weight_shap_total'))
df <- merge(df,tdf,by = 'V1',all = T,suffixes=c(i,i+1))
}
colnames(df) <- c('Gene',paste0('run',0:9))
In [1093]:
plot_df <- tidyr::gather(df, key = "variable", value = "value",-Gene)
In [1094]:
plot_df
| Gene | variable | value |
|---|---|---|
| <chr> | <chr> | <dbl> |
| ANXA2 | run0 | 1.034460e-03 |
| BARD1 | run0 | 1.626940e-03 |
| CD24 | run0 | 9.526794e-04 |
| CD9 | run0 | 3.365332e-03 |
| CEBPD | run0 | 5.203552e-04 |
| DAAM1 | run0 | 5.510014e-03 |
| DGKE | run0 | 1.200405e-03 |
| GTF2B | run0 | 1.464899e-03 |
| HIST1H1C | run0 | 5.280489e-04 |
| HSPA2 | run0 | 2.739355e-04 |
| KLF5 | run0 | 7.949345e-04 |
| LMO7 | run0 | 8.052727e-04 |
| MSH6 | run0 | 1.502611e-03 |
| MYLK | run0 | 5.435189e-05 |
| MYO1B | run0 | 1.198787e-03 |
| PLK2 | run0 | 7.665117e-04 |
| PMAIP1 | run0 | NA |
| PSMC4 | run0 | 1.613015e-03 |
| PTPRK | run0 | NA |
| RAB31 | run0 | 9.188358e-04 |
| RRBP1 | run0 | 3.582313e-04 |
| SCD5 | run0 | NA |
| SLC1A5 | run0 | 2.652719e-03 |
| TFAP2C | run0 | 2.591484e-04 |
| TFDP1 | run0 | 1.149151e-03 |
| TGM2 | run0 | 6.784385e-04 |
| TPM1 | run0 | NA |
| TRIB1 | run0 | 9.816274e-04 |
| TUBA1B | run0 | 1.910280e-03 |
| TUBB4B | run0 | 8.910743e-04 |
| ⋮ | ⋮ | ⋮ |
| CD24 | run9 | 0.0008012542 |
| CD9 | run9 | 0.0028918168 |
| CEBPD | run9 | 0.0005259405 |
| DAAM1 | run9 | NA |
| DGKE | run9 | 0.0014180804 |
| GTF2B | run9 | 0.0007559843 |
| HIST1H1C | run9 | 0.0005080224 |
| HSPA2 | run9 | 0.0011746135 |
| KLF5 | run9 | 0.0001498502 |
| LMO7 | run9 | 0.0003305257 |
| MSH6 | run9 | 0.0021751349 |
| MYLK | run9 | 0.0000477843 |
| MYO1B | run9 | 0.0009015045 |
| PLK2 | run9 | 0.0010625910 |
| PMAIP1 | run9 | NA |
| PSMC4 | run9 | 0.0009927616 |
| PTPRK | run9 | NA |
| RAB31 | run9 | 0.0014492702 |
| RRBP1 | run9 | 0.0005036239 |
| SCD5 | run9 | NA |
| SLC1A5 | run9 | 0.0029968812 |
| TFAP2C | run9 | 0.0004165673 |
| TFDP1 | run9 | 0.0011479995 |
| TGM2 | run9 | 0.0005497827 |
| TPM1 | run9 | 0.0004014363 |
| TRIB1 | run9 | 0.0008981097 |
| TUBA1B | run9 | 0.0013576953 |
| TUBB4B | run9 | 0.0004397604 |
| ULK1 | run9 | 0.0000858164 |
| ZC3HAV1 | run9 | 0.0003587815 |
In [1095]:
gl <- drivers[order(weight_shap_total_mean,decreasing = T),]$V1[1:10]
In [1096]:
tmp <- plot_df[plot_df$Gene %in% gl,]
tmp$Gene <- factor(tmp$Gene,levels = rev(gl))
In [1097]:
gl
- 'SLC1A5'
- 'BARD1'
- 'CD9'
- 'DAAM1'
- 'TUBA1B'
- 'MSH6'
- 'TFDP1'
- 'ANXA2'
- 'RAB31'
- 'PLK2'
In [1098]:
ridge_p <- ggplot(tmp, aes(x = value, y = Gene, fill = Gene)) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
ggsci::scale_fill_npg()+
xlab('SHAP weight')+
ylab('Driver genes')+
ggtitle("Weight through model with different seeds")+
scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5,hjust = 0.5),
axis.title.y = element_text(vjust = 5,hjust = 0.5),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(vjust = 0.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
legend.position = 'none',
panel.grid = element_blank()
)
ridge_p
Picking joint bandwidth of 0.000273
Warning message:
“Removed 6 rows containing non-finite outside the scale range
(`stat_density_ridges()`).”
Warning message:
“No shared levels found between `names(values)` of the manual scale and the
data's colour values.”
In [1099]:
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_ridge_p.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = ridge_p,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_ridge_p.png',
width =16/1.5, height =16/1.5)
Picking joint bandwidth of 0.000273 Warning message: “Removed 6 rows containing non-finite outside the scale range (`stat_density_ridges()`).” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.” Picking joint bandwidth of 0.000273 Warning message: “Removed 6 rows containing non-finite outside the scale range (`stat_density_ridges()`).” Warning message: “No shared levels found between `names(values)` of the manual scale and the data's colour values.”
In [822]:
drivers_exp <- fread(file.path(read_dir,'driver_summary_shap_total_addexp.csv'))
In [823]:
head(drivers_exp)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | time0_exp | time3_exp | time7_exp | time14_exp |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> | <dbl> | <dbl> |
| IGFBP5 | 0.011728111 | 0.0037999116 | 0.013004079 | 10 | FALSE | FALSE | FALSE | FALSE | -0.4614117 | -0.0006751149 | -0.24896230 | 0.19265650 |
| GSTM3 | 0.009201345 | 0.0016104131 | 0.004694919 | 10 | FALSE | FALSE | FALSE | FALSE | -0.4995057 | -0.4245171000 | -0.55196540 | 0.52083343 |
| HLA-B | 0.006611837 | 0.0014072035 | -0.003888358 | 10 | FALSE | FALSE | FALSE | FALSE | -1.3243920 | -0.1092498800 | -0.01360234 | 0.39492068 |
| CD24 | 0.004436760 | 0.0006784051 | 0.005839140 | 10 | FALSE | FALSE | FALSE | FALSE | -1.4788445 | 0.0453659200 | 0.24021030 | 0.27349910 |
| HIST1H4C | 0.004068935 | 0.0005085617 | -0.005728029 | 10 | FALSE | FALSE | FALSE | FALSE | 1.5583935 | -0.4402804400 | -0.44383672 | -0.02076823 |
| SAT1 | 0.003824356 | 0.0007941552 | -0.001987261 | 10 | FALSE | FALSE | FALSE | FALSE | -1.0865847 | 0.3337592500 | 0.26571733 | 0.01365761 |
In [3]:
GDSC_exp <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/Cell_line_RMA_proc_basalExp.txt')
In [4]:
head(GDSC_exp)
| GENE_SYMBOLS | GENE_title | DATA.906826 | DATA.687983 | DATA.910927 | DATA.1240138 | DATA.1240139 | DATA.906792 | DATA.910688 | DATA.1240135 | ⋯ | DATA.753584 | DATA.907044 | DATA.998184 | DATA.908145 | DATA.1659787 | DATA.1298157 | DATA.1480372 | DATA.1298533 | DATA.930299 | DATA.905954.1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| TSPAN6 | tetraspanin 6 [Source:HGNC Symbol;Acc:11858] | 7.632023 | 7.548671 | 8.712338 | 7.797142 | 7.729268 | 7.074533 | 3.285198 | 6.961606 | ⋯ | 7.105637 | 3.236503 | 3.038892 | 8.373223 | 6.932178 | 8.441628 | 8.422922 | 8.089255 | 3.112333 | 7.153127 |
| TNMD | tenomodulin [Source:HGNC Symbol;Acc:17757] | 2.964585 | 2.777716 | 2.643508 | 2.817923 | 2.957739 | 2.889677 | 2.828203 | 2.874751 | ⋯ | 2.798847 | 2.745137 | 2.976406 | 2.852552 | 2.622630 | 2.639276 | 2.879890 | 2.521169 | 2.870468 | 2.834285 |
| DPM1 | dolichyl-phosphate mannosyltransferase polypeptide 1, catalytic subunit [Source:HGNC Symbol;Acc:3005] | 10.379553 | 11.807341 | 9.880733 | 9.883471 | 10.418840 | 9.773987 | 10.264385 | 10.205931 | ⋯ | 10.486486 | 10.442951 | 10.311962 | 10.454830 | 10.418475 | 11.463742 | 10.557777 | 10.792750 | 9.873902 | 10.788218 |
| SCYL3 | SCY1-like 3 (S. cerevisiae) [Source:HGNC Symbol;Acc:19285] | 3.614794 | 4.066887 | 3.956230 | 4.063701 | 4.341500 | 4.270903 | 5.968168 | 3.715033 | ⋯ | 3.696835 | 4.624013 | 4.348524 | 3.858121 | 3.947561 | 4.425849 | 3.550390 | 4.443337 | 4.266828 | 4.100493 |
| C1orf112 | chromosome 1 open reading frame 112 [Source:HGNC Symbol;Acc:25565] | 3.380681 | 3.732485 | 3.236620 | 3.558414 | 3.840373 | 3.815055 | 3.011867 | 3.268449 | ⋯ | 3.726833 | 3.947744 | 3.806584 | 3.196988 | 3.814831 | 4.384732 | 4.247189 | 3.071359 | 3.230197 | 3.435795 |
| FGR | Gardner-Rasheed feline sarcoma viral (v-fgr) oncogene homolog [Source:HGNC Symbol;Acc:3697] | 3.324692 | 3.152404 | 3.241246 | 3.101247 | 3.001802 | 3.298915 | 9.565308 | 3.036333 | ⋯ | 3.245301 | 8.969347 | 3.562548 | 3.098083 | 3.170766 | 3.229511 | 3.176336 | 3.238305 | 3.027742 | 3.330279 |
In [5]:
GDSC_compounds <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/screened_compounds_rel_8.5.csv')
In [6]:
GDSC_compounds
| DRUG_ID | SCREENING_SITE | DRUG_NAME | SYNONYMS | TARGET | TARGET_PATHWAY |
|---|---|---|---|---|---|
| <int> | <chr> | <chr> | <chr> | <chr> | <chr> |
| 1 | MGH | Erlotinib | Tarceva, RG-1415, CP-358774, OSI-774, Ro-508231, R-1415 | EGFR | EGFR signaling |
| 3 | MGH | Rapamycin | AY-22989, Sirolimus, WY-090217, Torisel, Rapamune | MTORC1 | PI3K/MTOR signaling |
| 5 | MGH | Sunitinib | Sutent, Sunitinib Malate, SU-11248 | PDGFR, KIT, VEGFR, FLT3, RET, CSF1R | RTK signaling |
| 6 | MGH | PHA-665752 | PHA665752, PHA 665752 | MET | RTK signaling |
| 9 | MGH | MG-132 | LLL cpd, MG 132, MG132 | Proteasome, CAPN1 | Protein stability and degradation |
| 11 | MGH | Paclitaxel | BMS-181339-01, Taxol, Onxol, Paxene, Praxel, Abraxane | Microtubule stabiliser | Mitosis |
| 17 | MGH | Cyclopamine | SMO | Other | |
| 29 | MGH | AZ628 | AZ-628, AZ 628 | BRAF | ERK MAPK signaling |
| 30 | MGH | Sorafenib | Nexavar, 284461-73-0, BAY 43-9006 | PDGFR, KIT, VEGFR, RAF | RTK signaling |
| 32 | MGH | Tozasertib | MK 0457,MK-0457,MK-045, VX-680 VX 680 VX-68 | AURKA, AURKB, AURKC, others | Mitosis |
| 34 | MGH | Imatinib | Gleevec, STI-571 | ABL, KIT, PDGFR | Other, kinases |
| 35 | MGH | NVP-TAE684 | NVP-TAE 684, TAE684, TAE-684 | ALK | RTK signaling |
| 37 | MGH | Crizotinib | Xalkori, PF2341066, PF-2341066, PF 2341066 | MET, ALK, ROS1 | RTK signaling |
| 38 | MGH | Saracatinib | AZD0530, AZD-0530, AZ-10353926 | ABL, SRC | Other, kinases |
| 41 | MGH | S-Trityl-L-cysteine | NSC 83265, Tritylcysteine | KIF11 | Mitosis |
| 45 | MGH | Z-LLNle-CHO | Z-L-Norleucine-CHO, Gamma-Secretase Inhibitor 1 | gamma-secretase | Other |
| 51 | MGH | Dasatinib | BMS-354825-03, BMS-354825, Sprycel | ABL, SRC, Ephrins, PDGFR, KIT | RTK signaling |
| 52 | MGH | GNF-2 | KIN001-013 | BCR-ABL | ABL signaling |
| 53 | MGH | CGP-60474 | KIN001-019, CGP60474, CGP 60474 | CDK1,CDK2,CDK5,CDK7,CDK9, PKC | Cell cycle |
| 54 | MGH | CGP-082996 | CINK4, KIN001-021 | CDK4 | Cell cycle |
| 55 | MGH | A-770041 | KIN001-111 | LCK, FYN | Other, kinases |
| 56 | MGH | WH-4-023 | KIN001-112 | SRC, LCK | Other, kinases |
| 59 | MGH | WZ-1-84 | KIN001-123 | BMX | Other, kinases |
| 60 | MGH | BI-2536 | PLK1, PLK2, PLK3 | Cell cycle | |
| 62 | MGH | BMS-536924 | BMS 536924 | IGF1R, IR | IGF1R signaling |
| 63 | MGH | BMS-509744 | KIN001-127, ITK inhibitor | ITK | Other, kinases |
| 64 | MGH | CMK | KIN001-128 | RSK2 | Other, kinases |
| 71 | MGH | Pyrimethamine | Daraprim, Chloridine | Dihydrofolate reductase (DHFR) | Other |
| 83 | MGH | JW-7-52-1 | NA | MTOR | PI3K/MTOR signaling |
| 86 | MGH | A-443654 | KIN001-139 | AKT1, AKT2, AKT3 | PI3K/MTOR signaling |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 2107 | SANGER | LJI308 | RSK2, RSK1, RSK3 | PI3K/MTOR signaling | |
| 2109 | SANGER | AZ6102 | TNKS1, TNKS2 | WNT signaling | |
| 2110 | SANGER | GSK591 | EPZ015866, GSK3203591 | PMRT5 | Chromatin histone methylation |
| 2111 | SANGER | VE821 | VE 821, VE-821 | ATR | Genome integrity |
| 2112 | SANGER | VTP-A | Unclassified | ||
| 2113 | SANGER | VTP-B | Unclassified | ||
| 2145 | SANGER | PBD-288 | Unclassified | ||
| 2148 | SANGER | POMHEX | Unclassified | ||
| 2149 | SANGER | CT7033-2 | KDM4A, KDM4C, KDM4E, KDM3A, KDM6B | Chromatin histone methylation | |
| 2154 | SANGER | GSK-LSD1-2HCl | LSD1 | Chromatin histone methylation | |
| 2156 | SANGER | 5-azacytidine | DNA methyltransferases | Other | |
| 2157 | SANGER | A-366 | EHMT1, EHMT2 | Chromatin histone methylation | |
| 2158 | SANGER | CPI-637 | EP300 | Chromatin histone methylation | |
| 2159 | SANGER | UNC0379 | SETD8 | Chromatin histone methylation | |
| 2169 | SANGER | AZD6482 | AZD 6482, AZD-6482, AK-55409 | PI3Kbeta | PI3K/MTOR signaling |
| 2170 | SANGER | AT13148 | AKT1 | PI3K/MTOR signaling | |
| 2171 | SANGER | BMS-754807 | BMS-754807 | IGF1R, IR | IGF1R signaling |
| 2172 | SANGER | JQ1 | JQ-1, (+)-JQ-1 | BRD2, BRD3, BRD4, BRDT | Chromatin other |
| 2173 | SANGER | PFI-1 | BRD4 | Chromatin other | |
| 2174 | SANGER | IOX2 | IOX-2, IOX 2, AK176060 | EGLN1 | Other |
| 2175 | SANGER | CHIR-99021 | CT 99021, CHIR99021, CHIR 99021 | GSK3A, GSK3B | WNT signaling |
| 2177 | SANGER | SGC0946 | DOT1L | Chromatin histone methylation | |
| 2359 | SANGER | GSK2830371 | None | WIP1 | Other |
| 2360 | SANGER | THR-101 | WIMM synthesis | Mutant RAS | PI3K/MTOR signaling |
| 2361 | SANGER | THR-102 | WIMM synthesis | Mutant RAS | PI3K/MTOR signaling |
| 2362 | SANGER | THR-103 | WIMM synthesis | Mutant RAS | PI3K/MTOR signaling |
| 2438 | SANGER | ascorbate (vitamin C) | back-up solution from YWKim | anti-oxidant proteins | Other |
| 2439 | SANGER | glutathione | G6013, sigma | anti-oxidant proteins | Other |
| 2498 | SANGER | alpha-lipoic acid | aLA | Metabolism | Metabolism |
| 2499 | SANGER | N-acetyl cysteine | NAC | Metabolism | Metabolism |
In [7]:
GDSC_cellline <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/Cell_Lines_Details.xlsx')
In [8]:
head(GDSC_cellline)
| Sample.Name | COSMIC.identifier | Whole.Exome.Sequencing.(WES) | Copy.Number.Alterations.(CNA) | Gene.Expression | Methylation | Drug.Response | GDSC.Tissue.descriptor.1 | GDSC.Tissue.descriptor.2 | Cancer.Type.(matching.TCGA.label) | Microsatellite.instability.Status.(MSI) | Screen.Medium | Growth.Properties | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | |
| 1 | A253 | 906794 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | NA | MSS/MSI-L | D/F12 | Adherent |
| 2 | BB30-HNC | 753531 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| 3 | BB49-HNC | 753532 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| 4 | BHY | 753535 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| 5 | BICR10 | 1290724 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| 6 | BICR22 | 1240121 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
In [9]:
GDSC_drug1 <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/GDSC1_fitted_dose_response_27Oct23.xlsx')
In [10]:
GDSC_drug1
| DATASET | NLME_RESULT_ID | NLME_CURVE_ID | COSMIC_ID | CELL_LINE_NAME | SANGER_MODEL_ID | TCGA_DESC | DRUG_ID | DRUG_NAME | PUTATIVE_TARGET | PATHWAY_NAME | COMPANY_ID | WEBRELEASE | MIN_CONC | MAX_CONC | LN_IC50 | AUC | RMSE | Z_SCORE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | GDSC1 | 342 | 15580432 | 684057 | ES5 | SIDM00263 | UNCLASSIFIED | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.966813 | 0.985678 | 0.026081 | 1.299144 |
| 2 | GDSC1 | 342 | 15580806 | 684059 | ES7 | SIDM00269 | UNCLASSIFIED | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.692090 | 0.972690 | 0.110059 | 0.156076 |
| 3 | GDSC1 | 342 | 15581198 | 684062 | EW-11 | SIDM00203 | UNCLASSIFIED | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.477990 | 0.944459 | 0.087019 | -0.035912 |
| 4 | GDSC1 | 342 | 15581542 | 684072 | SK-ES-1 | SIDM01111 | UNCLASSIFIED | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.033564 | 0.950758 | 0.016290 | -0.434437 |
| 5 | GDSC1 | 342 | 15581930 | 687448 | COLO-829 | SIDM00909 | SKCM | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.966007 | 0.954778 | 0.180255 | 0.401702 |
| 6 | GDSC1 | 342 | 15585059 | 687562 | 8-MG-BA | SIDM00998 | GBM | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.493943 | 0.980200 | 0.041896 | -0.021607 |
| 7 | GDSC1 | 342 | 15585789 | 687568 | GB-1 | SIDM00581 | GBM | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.603243 | 0.984624 | 0.069293 | 0.973124 |
| 8 | GDSC1 | 342 | 15586874 | 687590 | U-87-MG | SIDM01189 | GBM | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.875674 | 0.982605 | 0.030822 | 1.217418 |
| 9 | GDSC1 | 342 | 15587948 | 687600 | NCI-H720 | SIDM01120 | UNCLASSIFIED | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.694213 | 0.986085 | 0.026592 | 1.054699 |
| 10 | GDSC1 | 342 | 15590086 | 687799 | NCI-H1648 | SIDM00746 | LUAD | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | -3.130315 | 0.349972 | 0.044001 | -5.064987 |
| 11 | GDSC1 | 342 | 15590484 | 687800 | NCI-H1650 | SIDM00745 | LUAD | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.661843 | 0.983298 | 0.033401 | 1.025671 |
| 12 | GDSC1 | 342 | 15591111 | 687804 | NCI-H1770 | SIDM00737 | UNCLASSIFIED | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.259282 | 0.988218 | 0.025678 | 0.664688 |
| 13 | GDSC1 | 342 | 15591503 | 687807 | NCI-H1838 | SIDM00769 | LUAD | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.322838 | 0.931209 | 0.057085 | -0.175040 |
| 14 | GDSC1 | 342 | 15594378 | 687983 | DMS-114 | SIDM00865 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.920006 | 0.988247 | 0.025773 | 1.257172 |
| 15 | GDSC1 | 342 | 15595456 | 687997 | NCI-H1092 | SIDM00653 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.681496 | 0.983316 | 0.041623 | 1.043295 |
| 16 | GDSC1 | 342 | 15595843 | 688006 | NCI-H1694 | SIDM00741 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.295288 | 0.987924 | 0.025592 | 0.696975 |
| 17 | GDSC1 | 342 | 15596237 | 688007 | NCI-H187 | SIDM00767 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.416401 | 0.983964 | 0.038981 | 0.805580 |
| 18 | GDSC1 | 342 | 15596624 | 688010 | NCI-H1963 | SIDM00760 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.762583 | 0.988447 | 0.060202 | 0.219288 |
| 19 | GDSC1 | 342 | 15597141 | 688013 | NCI-H209 | SIDM00706 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 1.702110 | 0.925430 | 0.029840 | -0.731659 |
| 20 | GDSC1 | 342 | 15597525 | 688014 | NCI-H2141 | SIDM00699 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.154961 | 0.987416 | 0.053634 | 0.571141 |
| 21 | GDSC1 | 342 | 15597920 | 688015 | NCI-H2171 | SIDM00733 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.623065 | 0.991685 | 0.021532 | 0.094180 |
| 22 | GDSC1 | 342 | 15598138 | 688018 | NCI-H2227 | SIDM00730 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.446735 | 0.982787 | 0.072259 | 0.832780 |
| 23 | GDSC1 | 342 | 15598448 | 688021 | NCI-H345 | SIDM00719 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 4.170068 | 0.986141 | 0.024686 | 1.481407 |
| 24 | GDSC1 | 342 | 15598841 | 688023 | NCI-H446 | SIDM00965 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.047965 | 0.982064 | 0.065505 | 0.475196 |
| 25 | GDSC1 | 342 | 15599234 | 688025 | NCI-H526 | SIDM01128 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.726717 | 0.975980 | 0.060841 | 0.187126 |
| 26 | GDSC1 | 342 | 15599619 | 688026 | NCI-H64 | SIDM01126 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 2.625623 | 0.994998 | 0.014885 | 0.096473 |
| 27 | GDSC1 | 342 | 15599907 | 688027 | NCI-H69 | SIDM01121 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.977617 | 0.987872 | 0.021174 | 1.308832 |
| 28 | GDSC1 | 342 | 15600286 | 688031 | NCI-H82 | SIDM01131 | SCLC | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 1.844167 | 0.935832 | 0.088138 | -0.604273 |
| 29 | GDSC1 | 342 | 15600925 | 688086 | SK-N-DZ | SIDM01100 | NB | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.106336 | 0.973860 | 0.029073 | 0.527538 |
| 30 | GDSC1 | 342 | 15601314 | 688087 | SK-N-FI | SIDM01099 | NB | 1 | Erlotinib | EGFR | EGFR signaling | 1045 | Y | 0.007813 | 2 | 3.115615 | 0.983788 | 0.021956 | 0.535858 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 333132 | GDSC1 | 342 | 15903595 | 1480362 | OV-56 | SIDM00475 | OV | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.671307 | 0.988140 | 0.032001 | 0.262398 |
| 333133 | GDSC1 | 342 | 15903903 | 1480364 | OVCA420 | SIDM00967 | OV | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 3.566044 | 0.930860 | 0.038729 | -1.334749 |
| 333134 | GDSC1 | 342 | 15904211 | 1480367 | OVCA433 | SIDM00966 | OV | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.509333 | 0.977166 | 0.069618 | 0.028339 |
| 333135 | GDSC1 | 342 | 15904518 | 1480371 | OVK-18 | SIDM00238 | OV | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.971570 | 0.977157 | 0.091919 | 0.696290 |
| 333136 | GDSC1 | 342 | 15904824 | 1480372 | PEO1 | SIDM00472 | OV | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.420722 | 0.976040 | 0.026130 | -0.099707 |
| 333137 | GDSC1 | 342 | 15905131 | 1480374 | UWB1.289 | SIDM00815 | OV | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 5.512290 | 0.975135 | 0.042982 | 1.477652 |
| 333138 | GDSC1 | 342 | 15905741 | 1503362 | OACp4C | SIDM00445 | ESCA | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.543185 | 0.972874 | 0.074415 | 0.077257 |
| 333139 | GDSC1 | 342 | 15906044 | 1503363 | OACM5-1 | SIDM00444 | ESCA | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 5.002370 | 0.982263 | 0.206642 | 0.740797 |
| 333140 | GDSC1 | 342 | 15906351 | 1503364 | SK-GT-2 | SIDM00393 | STAD | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.757195 | 0.981976 | 0.057134 | 0.386510 |
| 333141 | GDSC1 | 342 | 15906525 | 1503365 | SK-GT-4 | SIDM00483 | ESCA | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.804424 | 0.972438 | 0.030300 | 0.454758 |
| 333142 | GDSC1 | 342 | 15906835 | 1503366 | ESO26 | SIDM00539 | ESCA | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.810052 | 0.986740 | 0.025473 | 0.462891 |
| 333143 | GDSC1 | 342 | 15907145 | 1503367 | ESO51 | SIDM00538 | ESCA | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 5.420367 | 0.977101 | 0.072332 | 1.344820 |
| 333144 | GDSC1 | 342 | 15907403 | 1503368 | KYAE-1 | SIDM00530 | ESCA | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.461081 | 0.978863 | 0.031596 | -0.041387 |
| 333145 | GDSC1 | 342 | 15907705 | 1503369 | EMC-BAC-1 | SIDM00048 | LUAD | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 5.060163 | 0.978574 | 0.042571 | 0.824310 |
| 333146 | GDSC1 | 342 | 15908011 | 1503370 | EMC-BAC-2 | SIDM00047 | LUAD | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.050714 | 0.961589 | 0.071150 | -0.634382 |
| 333147 | GDSC1 | 342 | 15908316 | 1503371 | TE-4 | SIDM00250 | ESCA | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.524654 | 0.986404 | 0.080202 | 0.050479 |
| 333148 | GDSC1 | 342 | 15908623 | 1509073 | NCC010 | SIDM00231 | KIRC | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.617198 | 0.978494 | 0.095539 | 0.184209 |
| 333149 | GDSC1 | 342 | 15908794 | 1509074 | NCC021 | SIDM00232 | KIRC | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.451314 | 0.974422 | 0.029674 | -0.055500 |
| 333150 | GDSC1 | 342 | 15909101 | 1524414 | RCC-FG2 | SIDM00819 | KIRC | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 5.140318 | 0.978136 | 0.042883 | 0.940137 |
| 333151 | GDSC1 | 342 | 15909409 | 1524415 | RCC-JF | SIDM00818 | KIRC | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.798095 | 0.980377 | 0.044352 | 0.445612 |
| 333152 | GDSC1 | 342 | 15909717 | 1524416 | RCC-JW | SIDM00817 | KIRC | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 3.470532 | 0.949729 | 0.017115 | -1.472768 |
| 333153 | GDSC1 | 342 | 15910022 | 1524417 | RCC-ER | SIDM00820 | KIRC | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.127839 | 0.958409 | 0.042696 | -0.522934 |
| 333154 | GDSC1 | 342 | 15910329 | 1524418 | RCC-AB | SIDM00821 | KIRC | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.722547 | 0.970821 | 0.030351 | 0.336442 |
| 333155 | GDSC1 | 342 | 15910631 | 1524419 | RCC-MF | SIDM00816 | KIRC | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.284591 | 0.957327 | 0.057606 | -0.296421 |
| 333156 | GDSC1 | 342 | 15910934 | 1659817 | KMS-11 | SIDM00608 | MM | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 2.758604 | 0.934245 | 0.063815 | -2.501531 |
| 333157 | GDSC1 | 342 | 15911377 | 1659823 | SNU-1040 | SIDM00217 | COREAD | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 5.085294 | 0.972251 | 0.040661 | 0.860626 |
| 333158 | GDSC1 | 342 | 15912122 | 1660035 | SNU-61 | SIDM00194 | COREAD | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 5.725399 | 0.976109 | 0.045453 | 1.785602 |
| 333159 | GDSC1 | 342 | 15912431 | 1660036 | SNU-81 | SIDM00193 | COREAD | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.930753 | 0.970851 | 0.038612 | 0.637308 |
| 333160 | GDSC1 | 342 | 15912739 | 1674021 | SNU-C5 | SIDM00498 | COREAD | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.551784 | 0.972330 | 0.042649 | 0.089683 |
| 333161 | GDSC1 | 342 | 15913046 | 1789883 | DiFi | SIDM00049 | COREAD | 1531 | I-CBP112 | EP300, CBP | Chromatin histone acetylation | 1005 | Y | 0.039063 | 10 | 4.681069 | 0.980906 | 0.015312 | 0.276505 |
In [11]:
GDSC_drug2 <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/GDSC2_fitted_dose_response_27Oct23.xlsx')
In [12]:
GDSC_drug2
| DATASET | NLME_RESULT_ID | NLME_CURVE_ID | COSMIC_ID | CELL_LINE_NAME | SANGER_MODEL_ID | TCGA_DESC | DRUG_ID | DRUG_NAME | PUTATIVE_TARGET | PATHWAY_NAME | COMPANY_ID | WEBRELEASE | MIN_CONC | MAX_CONC | LN_IC50 | AUC | RMSE | Z_SCORE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | GDSC2 | 343 | 15946310 | 683667 | PFSK-1 | SIDM01132 | MB | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -1.463887 | 0.930220 | 0.089052 | 0.433123 |
| 2 | GDSC2 | 343 | 15946548 | 684052 | A673 | SIDM00848 | UNCLASSIFIED | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -4.869455 | 0.614970 | 0.111351 | -1.421100 |
| 3 | GDSC2 | 343 | 15946830 | 684057 | ES5 | SIDM00263 | UNCLASSIFIED | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -3.360586 | 0.791072 | 0.142855 | -0.599569 |
| 4 | GDSC2 | 343 | 15947087 | 684059 | ES7 | SIDM00269 | UNCLASSIFIED | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -5.044940 | 0.592660 | 0.135539 | -1.516647 |
| 5 | GDSC2 | 343 | 15947369 | 684062 | EW-11 | SIDM00203 | UNCLASSIFIED | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -3.741991 | 0.734047 | 0.128059 | -0.807232 |
| 6 | GDSC2 | 343 | 15947651 | 684072 | SK-ES-1 | SIDM01111 | UNCLASSIFIED | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -5.142961 | 0.582439 | 0.137581 | -1.570016 |
| 7 | GDSC2 | 343 | 15947932 | 687448 | COLO-829 | SIDM00909 | SKCM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -1.235034 | 0.867348 | 0.093470 | 0.557727 |
| 8 | GDSC2 | 343 | 15948212 | 687452 | 5637 | SIDM00807 | BLCA | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -2.632632 | 0.834067 | 0.076169 | -0.203221 |
| 9 | GDSC2 | 343 | 15948491 | 687455 | RT4 | SIDM01085 | BLCA | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -2.963191 | 0.821438 | 0.094466 | -0.383200 |
| 10 | GDSC2 | 343 | 15948772 | 687457 | SW780 | SIDM01160 | BLCA | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -1.449138 | 0.905050 | 0.074109 | 0.441154 |
| 11 | GDSC2 | 343 | 15949053 | 687459 | TCCSUP | SIDM01190 | BLCA | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -2.350633 | 0.843430 | 0.074831 | -0.049682 |
| 12 | GDSC2 | 343 | 15949334 | 687505 | C-33-A | SIDM00889 | CESC | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -3.380879 | 0.777806 | 0.091913 | -0.610618 |
| 13 | GDSC2 | 343 | 15949616 | 687506 | C-4-I | SIDM00905 | CESC | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -2.255690 | 0.891103 | 0.087072 | 0.002012 |
| 14 | GDSC2 | 343 | 15949896 | 687514 | ME-180 | SIDM00627 | CESC | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -3.223910 | 0.786658 | 0.135256 | -0.525153 |
| 15 | GDSC2 | 343 | 15950177 | 687561 | 42-MG-BA | SIDM00982 | GBM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -3.400220 | 0.777517 | 0.111615 | -0.621149 |
| 16 | GDSC2 | 343 | 15950458 | 687562 | 8-MG-BA | SIDM00998 | GBM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -4.256286 | 0.693915 | 0.110348 | -1.087249 |
| 17 | GDSC2 | 343 | 15950738 | 687563 | A172 | SIDM00799 | GBM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -2.999552 | 0.803407 | 0.083111 | -0.402998 |
| 18 | GDSC2 | 343 | 15951019 | 687568 | GB-1 | SIDM00581 | GBM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -3.055500 | 0.771900 | 0.107697 | -0.433460 |
| 19 | GDSC2 | 343 | 15951300 | 687586 | T98G | SIDM01171 | GBM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -2.060218 | 0.888774 | 0.075349 | 0.108440 |
| 20 | GDSC2 | 343 | 15951582 | 687588 | U-118-MG | SIDM01193 | GBM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -3.354018 | 0.748266 | 0.103497 | -0.595993 |
| 21 | GDSC2 | 343 | 15951863 | 687590 | U-87-MG | SIDM01189 | GBM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -2.450870 | 0.841792 | 0.120575 | -0.104258 |
| 22 | GDSC2 | 343 | 15952116 | 687592 | YKG-1 | SIDM00315 | GBM | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -2.784359 | 0.824085 | 0.091627 | -0.285832 |
| 23 | GDSC2 | 343 | 15952295 | 687596 | ChaGo-K-1 | SIDM00924 | UNCLASSIFIED | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | 0.528845 | 0.983500 | 0.063083 | 1.518103 |
| 24 | GDSC2 | 343 | 15952475 | 687600 | NCI-H720 | SIDM01120 | UNCLASSIFIED | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | 0.532792 | 0.984692 | 0.041052 | 1.520252 |
| 25 | GDSC2 | 343 | 15952744 | 687777 | Calu-3 | SIDM00922 | LUAD | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -1.629546 | 0.863243 | 0.096562 | 0.342928 |
| 26 | GDSC2 | 343 | 15953026 | 687780 | COR-L23 | SIDM00512 | UNCLASSIFIED | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -1.762080 | 0.882340 | 0.081469 | 0.270767 |
| 27 | GDSC2 | 343 | 15953307 | 687787 | LK-2 | SIDM00548 | LUSC | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -1.157607 | 0.922968 | 0.122499 | 0.599884 |
| 28 | GDSC2 | 343 | 15953588 | 687794 | NCI-H1437 | SIDM00734 | LUAD | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -1.839524 | 0.900130 | 0.095318 | 0.228601 |
| 29 | GDSC2 | 343 | 15953768 | 687798 | NCI-H1623 | SIDM00747 | LUAD | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -4.135944 | 0.687601 | 0.152799 | -1.021727 |
| 30 | GDSC2 | 343 | 15954049 | 687799 | NCI-H1648 | SIDM00746 | LUAD | 1003 | Camptothecin | TOP1 | DNA replication | 1046 | Y | 1e-04 | 0.1 | -1.962265 | 0.879493 | 0.108430 | 0.161773 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 242007 | GDSC2 | 343 | 16177468 | 1331037 | SU-DHL-6 | SIDM00407 | DLBC | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.355056 | 0.992049 | 0.115925 | -0.731217 |
| 242008 | GDSC2 | 343 | 16177747 | 1331038 | SU-DHL-8 | SIDM00423 | DLBC | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 8.167934 | 0.968546 | 0.173626 | -2.096806 |
| 242009 | GDSC2 | 343 | 16178028 | 1331039 | SUP-HD1 | SIDM00422 | UNCLASSIFIED | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 11.035636 | 0.981519 | 0.043595 | 1.202014 |
| 242010 | GDSC2 | 343 | 16178308 | 1331040 | SUP-M2 | SIDM00421 | UNCLASSIFIED | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.212775 | 0.995658 | 0.046115 | -0.894887 |
| 242011 | GDSC2 | 343 | 16178589 | 1331045 | TK | SIDM00323 | DLBC | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.573653 | 0.989811 | 0.149113 | -0.479756 |
| 242012 | GDSC2 | 343 | 16178871 | 1331048 | VAL | SIDM00416 | DLBC | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 8.224561 | 0.924414 | 0.104771 | -2.031665 |
| 242013 | GDSC2 | 343 | 16179150 | 1331049 | WIL2-NS | SIDM01102 | UNCLASSIFIED | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 8.123420 | 0.935963 | 0.101519 | -2.148011 |
| 242014 | GDSC2 | 343 | 16179318 | 1331050 | WSU-DLCL2 | SIDM00413 | DLBC | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.694330 | 0.991855 | 0.049626 | -0.340938 |
| 242015 | GDSC2 | 343 | 16179778 | 1479987 | DOV13 | SIDM00969 | OV | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 8.663452 | 0.944318 | 0.078590 | -1.526793 |
| 242016 | GDSC2 | 343 | 16180059 | 1479988 | Hey | SIDM00968 | OV | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.525712 | 0.935069 | 0.087330 | -0.534905 |
| 242017 | GDSC2 | 343 | 16181239 | 1480362 | OV-56 | SIDM00475 | OV | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.482878 | 0.948078 | 0.097027 | -0.584179 |
| 242018 | GDSC2 | 343 | 16181488 | 1480364 | OVCA420 | SIDM00967 | OV | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.618801 | 0.939100 | 0.068242 | -0.427821 |
| 242019 | GDSC2 | 343 | 16182207 | 1480372 | PEO1 | SIDM00472 | OV | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 11.659188 | 0.974363 | 0.082122 | 1.919309 |
| 242020 | GDSC2 | 343 | 16182669 | 1503361 | FLO-1 | SIDM01041 | ESCA | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.533827 | 0.971881 | 0.070754 | 0.624766 |
| 242021 | GDSC2 | 343 | 16182950 | 1503362 | OACp4C | SIDM00445 | ESCA | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.906682 | 0.968746 | 0.040929 | -0.096662 |
| 242022 | GDSC2 | 343 | 16183231 | 1503363 | OACM5-1 | SIDM00444 | ESCA | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.339901 | 0.938908 | 0.108254 | -0.748650 |
| 242023 | GDSC2 | 343 | 16183512 | 1503364 | SK-GT-2 | SIDM00393 | STAD | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.185576 | 0.975597 | 0.021798 | 0.224160 |
| 242024 | GDSC2 | 343 | 16183793 | 1503365 | SK-GT-4 | SIDM00483 | ESCA | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 11.362256 | 0.942377 | 0.080197 | 1.577737 |
| 242025 | GDSC2 | 343 | 16184073 | 1503366 | ESO26 | SIDM00539 | ESCA | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.315734 | 0.975361 | 0.103059 | 0.373886 |
| 242026 | GDSC2 | 343 | 16184354 | 1503367 | ESO51 | SIDM00538 | ESCA | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.441573 | 0.944978 | 0.153800 | -0.631693 |
| 242027 | GDSC2 | 343 | 16184636 | 1503368 | KYAE-1 | SIDM00530 | ESCA | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.490214 | 0.988352 | 0.081786 | 0.574596 |
| 242028 | GDSC2 | 343 | 16185096 | 1503370 | EMC-BAC-2 | SIDM00047 | LUAD | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.534000 | 0.967192 | 0.117754 | 0.624965 |
| 242029 | GDSC2 | 343 | 16185377 | 1503371 | TE-4 | SIDM00250 | ESCA | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.244155 | 0.968144 | 0.081944 | -0.858790 |
| 242030 | GDSC2 | 343 | 16186634 | 1524416 | RCC-JW | SIDM00817 | KIRC | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.518532 | 0.944765 | 0.067012 | -0.543164 |
| 242031 | GDSC2 | 343 | 16187633 | 1659818 | MM1S | SIDM01265 | MM | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 9.316959 | 0.988236 | 0.155465 | -0.775041 |
| 242032 | GDSC2 | 343 | 16188242 | 1659928 | SNU-175 | SIDM00216 | COREAD | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.127082 | 0.976746 | 0.074498 | 0.156872 |
| 242033 | GDSC2 | 343 | 16188695 | 1660034 | SNU-407 | SIDM00214 | COREAD | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 8.576377 | 0.913378 | 0.057821 | -1.626959 |
| 242034 | GDSC2 | 343 | 16188953 | 1660035 | SNU-61 | SIDM00194 | COREAD | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.519636 | 0.975001 | 0.058090 | 0.608442 |
| 242035 | GDSC2 | 343 | 16189493 | 1674021 | SNU-C5 | SIDM00498 | COREAD | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.694579 | 0.969969 | 0.101013 | 0.809684 |
| 242036 | GDSC2 | 343 | 16189775 | 1789883 | DiFi | SIDM00049 | COREAD | 2499 | N-acetyl cysteine | Metabolism | Metabolism | 1101 | Y | 2.001054 | 2000 | 10.034825 | 0.966988 | 0.089057 | 0.050746 |
In [374]:
any(GDSC_drug1$DRUG_NAME == 'Osimertinib')
FALSE
In [375]:
any(GDSC_drug2$DRUG_NAME == 'Osimertinib')
TRUE
In [13]:
GDSC_drug2 %>% dplyr::filter(GDSC_drug2$DRUG_NAME == 'Osimertinib')
| DATASET | NLME_RESULT_ID | NLME_CURVE_ID | COSMIC_ID | CELL_LINE_NAME | SANGER_MODEL_ID | TCGA_DESC | DRUG_ID | DRUG_NAME | PUTATIVE_TARGET | PATHWAY_NAME | COMPANY_ID | WEBRELEASE | MIN_CONC | MAX_CONC | LN_IC50 | AUC | RMSE | Z_SCORE |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| GDSC2 | 343 | 15946501 | 683667 | PFSK-1 | SIDM01132 | MB | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.279876 | 0.948851 | 0.083039 | -0.290963 |
| GDSC2 | 343 | 15946766 | 684052 | A673 | SIDM00848 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.372555 | 0.955487 | 0.075005 | -0.218362 |
| GDSC2 | 343 | 15947052 | 684057 | ES5 | SIDM00263 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.167834 | 0.947725 | 0.057817 | -0.378733 |
| GDSC2 | 343 | 15947305 | 684059 | ES7 | SIDM00269 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.865174 | 0.969978 | 0.024503 | 0.167538 |
| GDSC2 | 343 | 15947587 | 684062 | EW-11 | SIDM00203 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.335056 | 0.934942 | 0.039643 | -0.247737 |
| GDSC2 | 343 | 15947867 | 684072 | SK-ES-1 | SIDM01111 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.658029 | 0.972790 | 0.076845 | 0.005268 |
| GDSC2 | 343 | 15948147 | 687448 | COLO-829 | SIDM00909 | SKCM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.918938 | 0.938208 | 0.056445 | 0.993017 |
| GDSC2 | 343 | 15948426 | 687452 | 5637 | SIDM00807 | BLCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.457485 | 0.888955 | 0.070200 | -0.935194 |
| GDSC2 | 343 | 15948707 | 687455 | RT4 | SIDM01085 | BLCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.784537 | 0.981413 | 0.067026 | 0.887732 |
| GDSC2 | 343 | 15948988 | 687457 | SW780 | SIDM01160 | BLCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.301737 | 0.958979 | 0.058973 | 0.509525 |
| GDSC2 | 343 | 15949269 | 687459 | TCCSUP | SIDM01190 | BLCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.756258 | 0.965290 | 0.044146 | 0.865579 |
| GDSC2 | 343 | 15949552 | 687505 | C-33-A | SIDM00889 | CESC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.189240 | 0.883480 | 0.063777 | -1.145327 |
| GDSC2 | 343 | 15949831 | 687506 | C-4-I | SIDM00905 | CESC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.276588 | 0.906473 | 0.040211 | -1.076901 |
| GDSC2 | 343 | 15950113 | 687514 | ME-180 | SIDM00627 | CESC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.037413 | 0.860357 | 0.026376 | -1.322878 |
| GDSC2 | 343 | 15950393 | 687561 | 42-MG-BA | SIDM00982 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.475281 | 0.949391 | 0.042978 | -0.137890 |
| GDSC2 | 343 | 15950673 | 687562 | 8-MG-BA | SIDM00998 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.368088 | 0.982457 | 0.082000 | 0.561502 |
| GDSC2 | 343 | 15950954 | 687563 | A172 | SIDM00799 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.588013 | 0.942300 | 0.081864 | -0.049580 |
| GDSC2 | 343 | 15951235 | 687568 | GB-1 | SIDM00581 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.952566 | 0.975561 | 0.104329 | 1.802722 |
| GDSC2 | 343 | 15951518 | 687586 | T98G | SIDM01171 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.869108 | 0.981281 | 0.019718 | 0.953982 |
| GDSC2 | 343 | 15951798 | 687588 | U-118-MG | SIDM01193 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.796025 | 0.926795 | 0.059708 | 0.113368 |
| GDSC2 | 343 | 15952059 | 687590 | U-87-MG | SIDM01189 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.304401 | 0.958807 | 0.034969 | 0.511612 |
| GDSC2 | 343 | 15952260 | 687592 | YKG-1 | SIDM00315 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.641181 | 0.946104 | 0.080434 | -0.007930 |
| GDSC2 | 343 | 15952440 | 687596 | ChaGo-K-1 | SIDM00924 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.041674 | 0.969724 | 0.126241 | 0.305801 |
| GDSC2 | 343 | 15952683 | 687600 | NCI-H720 | SIDM01120 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.271699 | 0.976071 | 0.116991 | 0.485994 |
| GDSC2 | 343 | 15952962 | 687777 | Calu-3 | SIDM00922 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.105057 | 0.827993 | 0.098212 | -1.211272 |
| GDSC2 | 343 | 15953242 | 687780 | COR-L23 | SIDM00512 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.272615 | 0.954236 | 0.060843 | 0.486711 |
| GDSC2 | 343 | 15953524 | 687787 | LK-2 | SIDM00548 | LUSC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.440981 | 0.935244 | 0.065657 | -0.164759 |
| GDSC2 | 343 | 15953733 | 687794 | NCI-H1437 | SIDM00734 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.466979 | 0.973491 | 0.047336 | 0.638969 |
| GDSC2 | 343 | 15953984 | 687798 | NCI-H1623 | SIDM00747 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.196595 | 0.868151 | 0.052729 | -1.139565 |
| GDSC2 | 343 | 15954155 | 687799 | NCI-H1648 | SIDM00746 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.701592 | 0.800871 | 0.093298 | -1.843171 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| GDSC2 | 343 | 16182606 | 1503361 | FLO-1 | SIDM01041 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.859503 | 0.945865 | 0.059545 | 0.163096 |
| GDSC2 | 343 | 16182886 | 1503362 | OACp4C | SIDM00445 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.822752 | 0.956546 | 0.075153 | 0.134306 |
| GDSC2 | 343 | 16183167 | 1503363 | OACM5-1 | SIDM00444 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.211594 | 0.974548 | 0.028010 | 1.222273 |
| GDSC2 | 343 | 16183448 | 1503364 | SK-GT-2 | SIDM00393 | STAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -3.066920 | 0.548453 | 0.117406 | -3.696080 |
| GDSC2 | 343 | 16183730 | 1503365 | SK-GT-4 | SIDM00483 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.863469 | 0.882733 | 0.073594 | 0.166202 |
| GDSC2 | 343 | 16184009 | 1503366 | ESO26 | SIDM00539 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.239166 | 0.934399 | 0.042767 | -0.322854 |
| GDSC2 | 343 | 16184290 | 1503367 | ESO51 | SIDM00538 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.157952 | 0.975412 | 0.024334 | 1.180251 |
| GDSC2 | 343 | 16184573 | 1503368 | KYAE-1 | SIDM00530 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -1.560172 | 0.739831 | 0.039288 | -2.515751 |
| GDSC2 | 343 | 16184782 | 1503369 | EMC-BAC-1 | SIDM00048 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.042516 | 0.856887 | 0.132649 | -1.326876 |
| GDSC2 | 343 | 16185032 | 1503370 | EMC-BAC-2 | SIDM00047 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.664314 | 0.886320 | 0.090450 | -0.773171 |
| GDSC2 | 343 | 16185313 | 1503371 | TE-4 | SIDM00250 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -1.707318 | 0.723921 | 0.058785 | -2.631019 |
| GDSC2 | 343 | 16185523 | 1503373 | U-CH2 | SIDM01185 | NA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.817128 | 0.887444 | 0.082043 | -0.653462 |
| GDSC2 | 343 | 16185779 | 1509073 | NCC010 | SIDM00231 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.574713 | 0.921736 | 0.083888 | -0.843361 |
| GDSC2 | 343 | 16185959 | 1509074 | NCC021 | SIDM00232 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.338782 | 0.953848 | 0.078305 | 0.538544 |
| GDSC2 | 343 | 16186139 | 1524414 | RCC-FG2 | SIDM00819 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.846549 | 0.911654 | 0.134748 | -0.630415 |
| GDSC2 | 343 | 16186319 | 1524415 | RCC-JF | SIDM00818 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.926748 | 0.923610 | 0.066450 | -0.567591 |
| GDSC2 | 343 | 16186571 | 1524416 | RCC-JW | SIDM00817 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.237197 | 0.954133 | 0.037277 | 0.458967 |
| GDSC2 | 343 | 16186780 | 1524417 | RCC-ER | SIDM00820 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.680820 | 0.956640 | 0.019253 | 0.023121 |
| GDSC2 | 343 | 16186960 | 1524418 | RCC-AB | SIDM00821 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.856423 | 0.888532 | 0.088464 | -0.622681 |
| GDSC2 | 343 | 16187140 | 1524419 | RCC-MF | SIDM00816 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.965417 | 0.964731 | 0.062086 | 0.246064 |
| GDSC2 | 343 | 16187320 | 1659817 | KMS-11 | SIDM00608 | MM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.576469 | 0.967225 | 0.059140 | -0.058623 |
| GDSC2 | 343 | 16187570 | 1659818 | MM1S | SIDM01265 | MM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.862097 | 0.967879 | 0.008336 | -0.618235 |
| GDSC2 | 343 | 16187776 | 1659819 | OCI-LY7 | SIDM00459 | DLBC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.948492 | 0.975369 | 0.019785 | -0.550557 |
| GDSC2 | 343 | 16188178 | 1659928 | SNU-175 | SIDM00216 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.211949 | 0.857902 | 0.056394 | -1.459603 |
| GDSC2 | 343 | 16188388 | 1659929 | SNU-283 | SIDM00215 | NA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.619565 | 0.913037 | 0.081642 | -0.808226 |
| GDSC2 | 343 | 16188640 | 1660034 | SNU-407 | SIDM00214 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.115710 | 0.919957 | 0.050110 | -0.419564 |
| GDSC2 | 343 | 16188910 | 1660035 | SNU-61 | SIDM00194 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.378973 | 0.964479 | 0.044681 | 0.570028 |
| GDSC2 | 343 | 16189177 | 1660036 | SNU-81 | SIDM00193 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.891271 | 0.975764 | 0.057990 | 0.187981 |
| GDSC2 | 343 | 16189429 | 1674021 | SNU-C5 | SIDM00498 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.503527 | 0.957638 | 0.057810 | 0.667600 |
| GDSC2 | 343 | 16189711 | 1789883 | DiFi | SIDM00049 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -2.061857 | 0.665544 | 0.081299 | -2.908751 |
In [14]:
GDSC_cellline %>% dplyr::filter(GDSC_cellline$Gene.Expression == 'Y')
| Sample.Name | COSMIC.identifier | Whole.Exome.Sequencing.(WES) | Copy.Number.Alterations.(CNA) | Gene.Expression | Methylation | Drug.Response | GDSC.Tissue.descriptor.1 | GDSC.Tissue.descriptor.2 | Cancer.Type.(matching.TCGA.label) | Microsatellite.instability.Status.(MSI) | Screen.Medium | Growth.Properties |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> |
| A253 | 906794 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | NA | MSS/MSI-L | D/F12 | Adherent |
| BB30-HNC | 753531 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| BB49-HNC | 753532 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| BHY | 753535 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| BICR10 | 1290724 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| BICR22 | 1240121 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| BICR31 | 1290725 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| BICR78 | 1240122 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| Ca9-22 | 753538 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| CAL-27 | 910916 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| CAL-33 | 753541 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| Detroit562 | 906837 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| DOK | 910936 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| FADU | 906863 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| H3118 | 1240140 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | NA | MSS/MSI-L | R | Adherent |
| HN | 907059 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| HO-1-N-1 | 924111 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| HO-1-u-1 | 753561 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| HSC-2 | 753562 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| HSC-3 | 907061 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| HSC-4 | 907062 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| JHU-011 | 1240161 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | R | Adherent |
| JHU-022 | 1240162 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | R | Adherent |
| KON | 1298215 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| KOSC-2 | 753570 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | R | Adherent |
| LB771-HNC | 753583 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| OSC-19 | 1298362 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| OSC-20 | 1240196 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | D/F12 | Adherent |
| PCI-15A | 1240204 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | R | Adherent |
| PCI-30 | 1298529 | Y | Y | Y | Y | Y | aero_dig_tract | head and neck | HNSC | MSS/MSI-L | R | Adherent |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| JHH-4 | 1240158 | Y | N | Y | Y | Y | digestive_system | liver | LIHC | MSS/MSI-L | D/F12 | Adherent |
| RCC-JF | 1524415 | Y | Y | Y | N | Y | kidney | kidney | KIRC | MSS/MSI-L | D/F12 | Adherent |
| SW156 | 1240220 | Y | N | Y | Y | Y | kidney | kidney | KIRC | MSS/MSI-L | D/F12 | Adherent |
| HOP-62 | 905972 | Y | Y | Y | N | Y | lung_NSCLC | lung_NSCLC_adenocarcinoma | LUAD | MSS/MSI-L | R | Adherent |
| PC-3 [JPC-3] | 1240202 | Y | Y | Y | N | Y | lung_NSCLC | lung_NSCLC_adenocarcinoma | LUAD | MSS/MSI-L | D/F12 | Adherent |
| NCI-H740 | 1331055 | Y | Y | Y | Y | N | lung_SCLC | lung_small_cell_carcinoma | SCLC | MSS/MSI-L | R | Suspension |
| COR-L95 | 1297439 | Y | Y | Y | N | Y | lung_SCLC | lung_small_cell_carcinoma | SCLC | MSS/MSI-L | R | Adherent |
| NCI-H1304 | 753599 | Y | Y | Y | N | Y | lung_SCLC | lung_small_cell_carcinoma | SCLC | MSS/MSI-L | D/F12 | Suspension |
| SHP-77 | 724872 | Y | Y | Y | N | Y | lung_SCLC | lung_small_cell_carcinoma | SCLC | MSS/MSI-L | R | Semi-Adherent |
| SW1271 | 1299062 | Y | Y | Y | N | Y | lung_SCLC | lung_small_cell_carcinoma | SCLC | MSS/MSI-L | D/F12 | Adherent |
| D-423MG | 946372 | Y | Y | Y | N | Y | nervous_system | glioma | GBM | MSS/MSI-L | D/F12 | Adherent |
| COLO-783 | 1240125 | Y | Y | Y | N | Y | skin | melanoma | SKCM | MSS/MSI-L | R | Adherent |
| Hs633T | 1240149 | Y | N | Y | Y | Y | soft_tissue | fibrosarcoma | NA | MSS/MSI-L | D/F12 | Adherent |
| RL95-2 | 930082 | Y | Y | Y | N | Y | urogenital_system | endometrium | NA | MSI-H | D/F12 | Adherent |
| DOV13 | 1479987 | Y | Y | Y | N | Y | urogenital_system | ovary | OV | MSS/MSI-L | D/F12 | Adherent |
| Hey | 1479988 | Y | Y | Y | N | Y | urogenital_system | ovary | OV | MSS/MSI-L | R | Adherent |
| NCI-H660 | 1330975 | Y | Y | Y | Y | N | urogenital_system | prostate | NA | MSS/MSI-L | D/F12 | Suspension |
| JHU-029 | 1298156 | Y | Y | Y | N | Y | aero_dig_tract | head and neck | HNSC | NA | R | Adherent |
| SCC90 | 1299052 | Y | Y | Y | N | Y | aero_dig_tract | head and neck | HNSC | NA | R | Adherent |
| MOLM-16 | 1330948 | Y | Y | Y | N | Y | leukemia | acute_myeloid_leukaemia | LAML | NA | R | Suspension |
| OCI-LY7 | 1659819 | Y | Y | Y | N | Y | lymphoma | B_cell_lymphoma | DLBC | NA | D/F12 | Suspension |
| KMS-11 | 1659817 | Y | Y | Y | N | Y | myeloma | myeloma | MM | NA | R | Suspension |
| CHSA0011 | 1290767 | Y | Y | Y | N | Y | bone | chondrosarcoma | NA | NA | R | Adherent |
| CHSA0108 | 1290768 | Y | Y | Y | N | Y | bone | chondrosarcoma | NA | NA | R | Adherent |
| JIMT-1 | 1298157 | Y | Y | Y | N | Y | breast | breast | BRCA | NA | D/F12 | Adherent |
| CL-34 | 1290771 | Y | Y | Y | N | Y | large_intestine | large_intestine | COAD/READ | NA | D/F12 | Adherent |
| VMRC-LCD | 713869 | Y | Y | Y | N | Y | lung_NSCLC | lung_NSCLC_adenocarcinoma | LUAD | NA | D/F12 | Adherent |
| K2 | 1298160 | Y | Y | Y | N | Y | skin | melanoma | SKCM | NA | D/F12 | Adherent |
| STS-0421 | 1299061 | Y | Y | Y | N | Y | soft_tissue | soft_tissue_other | NA | NA | R | Adherent |
| KP-2 | 1298218 | Y | N | Y | N | Y | pancreas | pancreas | PAAD | NA | D/F12 | Adherent |
In [15]:
luad_Osimertinib <- GDSC_drug2 %>% dplyr::filter(GDSC_drug2$COSMIC_ID %in% substring(colnames(GDSC_exp),6)) %>%
dplyr::filter(DRUG_NAME == 'Osimertinib') %>%
dplyr::filter(TCGA_DESC == 'LUAD')
luad_Osimertinib
| DATASET | NLME_RESULT_ID | NLME_CURVE_ID | COSMIC_ID | CELL_LINE_NAME | SANGER_MODEL_ID | TCGA_DESC | DRUG_ID | DRUG_NAME | PUTATIVE_TARGET | PATHWAY_NAME | COMPANY_ID | WEBRELEASE | MIN_CONC | MAX_CONC | LN_IC50 | AUC | RMSE | Z_SCORE |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| GDSC2 | 343 | 15952962 | 687777 | Calu-3 | SIDM00922 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.105057 | 0.827993 | 0.098212 | -1.211272 |
| GDSC2 | 343 | 15953984 | 687798 | NCI-H1623 | SIDM00747 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.196595 | 0.868151 | 0.052729 | -1.139565 |
| GDSC2 | 343 | 15954155 | 687799 | NCI-H1648 | SIDM00746 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.701592 | 0.800871 | 0.093298 | -1.843171 |
| GDSC2 | 343 | 15954434 | 687800 | NCI-H1650 | SIDM00745 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.407872 | 0.844857 | 0.134400 | -0.974058 |
| GDSC2 | 343 | 15954715 | 687802 | NCI-H1693 | SIDM00742 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.111300 | 0.861017 | 0.104960 | -0.423019 |
| GDSC2 | 343 | 15955064 | 687807 | NCI-H1838 | SIDM00769 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.462617 | 0.960185 | 0.020880 | 1.418915 |
| GDSC2 | 343 | 15955347 | 687812 | NCI-H2085 | SIDM00709 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.773792 | 0.939289 | 0.042010 | 0.095952 |
| GDSC2 | 343 | 15955905 | 687816 | NCI-H2228 | SIDM00729 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.072226 | 0.954529 | 0.060727 | 1.113097 |
| GDSC2 | 343 | 15956075 | 687819 | NCI-H2342 | SIDM00727 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.648910 | 0.966616 | 0.072543 | 0.781487 |
| GDSC2 | 343 | 15956243 | 687820 | NCI-H2347 | SIDM00726 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.553966 | 0.946425 | 0.075520 | 1.490474 |
| GDSC2 | 343 | 15956524 | 687821 | NCI-H2405 | SIDM00724 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.333193 | 0.930423 | 0.021137 | 1.317529 |
| GDSC2 | 343 | 15962714 | 713869 | VMRC-LCD | SIDM00320 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.788954 | 0.968042 | 0.053503 | 0.891192 |
| GDSC2 | 343 | 15964063 | 722045 | NCI-H2030 | SIDM00715 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.675624 | 0.881735 | 0.057337 | -0.764311 |
| GDSC2 | 343 | 15964314 | 722046 | NCI-H2122 | SIDM00702 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.751953 | 0.935293 | 0.041326 | -0.704518 |
| GDSC2 | 343 | 15964523 | 722058 | NCI-H1734 | SIDM00739 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.873261 | 0.980054 | 0.096387 | 0.173873 |
| GDSC2 | 343 | 15964774 | 722066 | NCI-H650 | SIDM01124 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.874454 | 0.985684 | 0.033043 | 0.958170 |
| GDSC2 | 343 | 15966080 | 724834 | NCI-H2087 | SIDM00708 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.411423 | 0.970948 | 0.045980 | 1.378811 |
| GDSC2 | 343 | 15967093 | 724859 | Calu-6 | SIDM00921 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.481590 | 0.968356 | 0.035590 | 1.433777 |
| GDSC2 | 343 | 15967657 | 724866 | NCI-H1355 | SIDM00645 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.228194 | 0.956801 | 0.049057 | 1.235276 |
| GDSC2 | 343 | 15967939 | 724868 | NCI-H1792 | SIDM00771 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.193570 | 0.978127 | 0.042502 | 0.424791 |
| GDSC2 | 343 | 15968919 | 724873 | NCI-H2009 | SIDM00756 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.373773 | 0.961620 | 0.017152 | 0.565955 |
| GDSC2 | 343 | 15969127 | 724874 | NCI-H2291 | SIDM00728 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.810679 | 0.898188 | 0.063197 | 0.124848 |
| GDSC2 | 343 | 15969377 | 724878 | SW1573 | SIDM01163 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 4.609979 | 0.977902 | 0.047665 | 2.317715 |
| GDSC2 | 343 | 15982957 | 753592 | LXF-289 | SIDM00339 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.343446 | 0.975225 | 0.064421 | 0.542198 |
| GDSC2 | 343 | 15983880 | 753600 | NCI-H1563 | SIDM00751 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.882932 | 0.917099 | 0.086269 | -0.601914 |
| GDSC2 | 343 | 15984699 | 753608 | PC-14 | SIDM00237 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -3.304365 | 0.518041 | 0.157858 | -3.882086 |
| GDSC2 | 343 | 15990460 | 905942 | NCI-H23 | SIDM00138 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.942427 | 0.988078 | 0.042001 | 1.011417 |
| GDSC2 | 343 | 15990920 | 905944 | NCI-H522 | SIDM00116 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.852038 | 0.960971 | 0.038513 | 0.157247 |
| GDSC2 | 343 | 15992334 | 905949 | A549 | SIDM00903 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.212945 | 0.919610 | 0.083585 | -0.343394 |
| GDSC2 | 343 | 15996623 | 905967 | NCI-H322M | SIDM00117 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -1.380772 | 0.723152 | 0.066003 | -2.375215 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| GDSC2 | 343 | 15998008 | 905972 | HOP-62 | SIDM00133 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.742171 | 0.955952 | 0.045442 | 0.854544 |
| GDSC2 | 343 | 16004576 | 906791 | ABC-1 | SIDM00494 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.841505 | 0.969501 | 0.076861 | 0.932359 |
| GDSC2 | 343 | 16007374 | 906805 | COR-L105 | SIDM00513 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -1.382719 | 0.707402 | 0.069176 | -2.376740 |
| GDSC2 | 343 | 16039604 | 907786 | LC-2-ad | SIDM00297 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.639399 | 0.964106 | 0.035397 | -0.009326 |
| GDSC2 | 343 | 16055468 | 908460 | NCI-H441 | SIDM00925 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 5.459908 | 0.965804 | 0.033519 | 2.983518 |
| GDSC2 | 343 | 16056177 | 908463 | NCI-H1793 | SIDM00755 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.702049 | 0.954930 | 0.049446 | 1.606477 |
| GDSC2 | 343 | 16056460 | 908465 | NCI-H358 | SIDM00718 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.645906 | 0.860003 | 0.038071 | -0.787591 |
| GDSC2 | 343 | 16057830 | 908472 | NCI-H1573 | SIDM00749 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.168900 | 0.982703 | 0.024483 | 1.188827 |
| GDSC2 | 343 | 16058113 | 908473 | NCI-H1666 | SIDM00743 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.447561 | 0.834463 | 0.079597 | -0.942967 |
| GDSC2 | 343 | 16058565 | 908475 | NCI-H1755 | SIDM00738 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.890945 | 0.940001 | 0.085464 | 0.187726 |
| GDSC2 | 343 | 16058847 | 908476 | NCI-H1993 | SIDM00758 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.091601 | 0.952126 | 0.094151 | 0.344912 |
| GDSC2 | 343 | 16068487 | 909721 | SK-LU-1 | SIDM01108 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 4.560097 | 0.969773 | 0.032863 | 2.278639 |
| GDSC2 | 343 | 16082794 | 910399 | NCI-H838 | SIDM01150 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.636913 | 0.954137 | 0.076374 | -0.011274 |
| GDSC2 | 343 | 16092829 | 910900 | NCI-H1651 | SIDM00744 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.780943 | 0.961797 | 0.056277 | 0.101554 |
| GDSC2 | 343 | 16097910 | 910931 | RERF-LC-MS | SIDM00355 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.983289 | 0.968638 | 0.037446 | 0.260064 |
| GDSC2 | 343 | 16107124 | 924244 | NCI-H1975 | SIDM00759 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -3.272124 | 0.521202 | 0.169023 | -3.856830 |
| GDSC2 | 343 | 16129315 | 1240145 | HCC-44 | SIDM01069 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.473396 | 0.970796 | 0.036816 | 0.643996 |
| GDSC2 | 343 | 16129588 | 1240146 | HCC-827 | SIDM01067 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -3.590724 | 0.485425 | 0.194687 | -4.106409 |
| GDSC2 | 343 | 16136682 | 1240185 | NCI-H1944 | SIDM00762 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.088707 | 0.900065 | 0.089288 | -0.440717 |
| GDSC2 | 343 | 16137246 | 1240187 | NCI-H2023 | SIDM00753 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.062089 | 0.965789 | 0.032283 | 1.105156 |
| GDSC2 | 343 | 16137797 | 1240190 | NCI-H3122 | SIDM00137 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.151088 | 0.949050 | 0.074977 | 0.391512 |
| GDSC2 | 343 | 16140155 | 1240202 | PC-3_[JPC-3] | SIDM00361 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -2.604536 | 0.591882 | 0.105147 | -3.333866 |
| GDSC2 | 343 | 16144709 | 1247873 | H3255 | SIDM00046 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -2.523354 | 0.594091 | 0.078894 | -3.270271 |
| GDSC2 | 343 | 16150565 | 1290908 | HCC-78 | SIDM01068 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.963438 | 0.901244 | 0.049513 | -0.538849 |
| GDSC2 | 343 | 16157570 | 1298347 | NCI-H1435 | SIDM00658 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.215420 | 0.988440 | 0.028504 | 0.441907 |
| GDSC2 | 343 | 16157848 | 1298348 | NCI-H1568 | SIDM00750 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.469193 | 0.855017 | 0.078173 | -0.926021 |
| GDSC2 | 343 | 16158298 | 1298350 | NCI-H1781 | SIDM00754 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.348410 | 0.824295 | 0.055829 | -1.566501 |
| GDSC2 | 343 | 16162376 | 1298537 | RERF-LC-KJ | SIDM00356 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.415262 | 0.946008 | 0.067245 | -0.184907 |
| GDSC2 | 343 | 16184782 | 1503369 | EMC-BAC-1 | SIDM00048 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.042516 | 0.856887 | 0.132649 | -1.326876 |
| GDSC2 | 343 | 16185032 | 1503370 | EMC-BAC-2 | SIDM00047 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.664314 | 0.886320 | 0.090450 | -0.773171 |
In [16]:
luad_Osimertinib_exp <- GDSC_exp %>% dplyr::select(c(1,which(substring(colnames(GDSC_exp),6) %in% luad_Osimertinib$COSMIC_ID)))
head(luad_Osimertinib_exp)
| GENE_SYMBOLS | DATA.908460 | DATA.908465 | DATA.910399 | DATA.687816 | DATA.724873 | DATA.722045 | DATA.724868 | DATA.724866 | DATA.687798 | ⋯ | DATA.687819 | DATA.905942 | DATA.905944 | DATA.924244 | DATA.713869 | DATA.1298347 | DATA.1298537 | DATA.687777 | DATA.724834 | DATA.722046 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| TSPAN6 | 8.663931 | 8.578107 | 7.336914 | 7.077455 | 7.934625 | 7.121621 | 7.247945 | 7.321865 | 6.905705 | ⋯ | 7.544027 | 7.319616 | 8.827198 | 7.923672 | 4.353046 | 7.753878 | 8.298252 | 9.041562 | 7.679445 | 7.730895 |
| TNMD | 3.074756 | 2.862169 | 2.794700 | 2.885422 | 3.154009 | 3.039269 | 2.704445 | 2.720055 | 2.592718 | ⋯ | 3.101391 | 2.780444 | 2.891027 | 2.709755 | 2.652075 | 2.672617 | 2.800777 | 2.802379 | 2.691326 | 2.691445 |
| DPM1 | 10.163427 | 10.150886 | 10.865909 | 9.204721 | 9.894184 | 9.881158 | 10.026557 | 9.025393 | 9.826111 | ⋯ | 9.425653 | 10.482441 | 9.710483 | 10.040750 | 9.548978 | 9.612500 | 10.267517 | 9.974280 | 9.824226 | 10.420010 |
| SCYL3 | 4.965831 | 5.208023 | 4.271641 | 4.256899 | 4.744393 | 3.666654 | 3.607068 | 3.948645 | 4.541889 | ⋯ | 4.517643 | 3.734341 | 3.997366 | 3.647858 | 4.188702 | 4.888457 | 4.207811 | 4.291778 | 4.545904 | 3.648124 |
| C1orf112 | 3.655091 | 3.374075 | 3.442324 | 3.112787 | 3.166096 | 3.330703 | 3.323256 | 3.058993 | 3.694343 | ⋯ | 3.740183 | 3.651500 | 4.008008 | 3.116660 | 3.435944 | 3.611179 | 2.994088 | 3.784500 | 4.072171 | 4.008678 |
| FGR | 2.991510 | 2.984719 | 3.177299 | 2.960159 | 3.110312 | 3.197060 | 3.087821 | 3.263744 | 3.245974 | ⋯ | 3.336917 | 3.135225 | 3.274437 | 3.346359 | 3.081963 | 3.442439 | 3.172185 | 3.272233 | 3.293596 | 3.260190 |
In [436]:
luad_Osimertinib_exp_SLC1A5 <- luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS == 'SLC1A5') %>% t() %>% as.data.frame()
In [437]:
luad_Osimertinib_exp_SLC1A5 <- luad_Osimertinib_exp_SLC1A5 %>% mutate(COSMIC_ID = substring(rownames(luad_Osimertinib_exp_SLC1A5),6))
luad_Osimertinib_exp_SLC1A5 <- luad_Osimertinib_exp_SLC1A5[2:nrow(luad_Osimertinib_exp_SLC1A5),] %>% apply(2,as.numeric)
colnames(luad_Osimertinib_exp_SLC1A5) <- c('SLC1A5','COSMIC_ID')
In [438]:
luad_Osimertinib_exp_SLC1A5
| SLC1A5 | COSMIC_ID |
|---|---|
| 7.975696 | 908460 |
| 8.479559 | 908465 |
| 7.921446 | 910399 |
| 8.292501 | 687816 |
| 8.027455 | 724873 |
| 6.631435 | 722045 |
| 7.023257 | 724868 |
| 6.888380 | 724866 |
| 7.311690 | 687798 |
| 7.524080 | 908475 |
| 7.615405 | 908463 |
| 7.948984 | 753608 |
| 6.803541 | 910931 |
| 6.233322 | 1240202 |
| 6.868018 | 908472 |
| 5.548601 | 910900 |
| 6.404932 | 687799 |
| 7.906778 | 722058 |
| 5.958910 | 753600 |
| 7.294597 | 1298348 |
| 7.409463 | 1240185 |
| 7.990055 | 908476 |
| 7.260682 | 1298350 |
| 8.770801 | 907786 |
| 6.959397 | 753592 |
| 6.980728 | 906805 |
| 6.845378 | 724859 |
| 7.646560 | 905949 |
| 6.658691 | 1240146 |
| 7.316419 | 909721 |
| ⋮ | ⋮ |
| 6.277130 | 905972 |
| 7.823917 | 905970 |
| 7.501640 | 1240145 |
| 7.221498 | 1290908 |
| 6.656378 | 1503370 |
| 7.991184 | 1247873 |
| 8.246154 | 906791 |
| 4.751698 | 1503369 |
| 6.452249 | 687820 |
| 6.779306 | 908473 |
| 7.837235 | 687800 |
| 7.370621 | 687821 |
| 7.476429 | 905967 |
| 5.368187 | 687807 |
| 7.382293 | 1240187 |
| 6.394580 | 722066 |
| 6.565660 | 724874 |
| 7.994473 | 1240190 |
| 6.296948 | 687802 |
| 7.003254 | 687812 |
| 7.360163 | 687819 |
| 5.701877 | 905942 |
| 5.595287 | 905944 |
| 6.619834 | 924244 |
| 7.869154 | 713869 |
| 9.138640 | 1298347 |
| 8.486290 | 1298537 |
| 7.738085 | 687777 |
| 7.078411 | 724834 |
| 7.740654 | 722046 |
In [440]:
cor_df <- luad_Osimertinib %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>%
merge(luad_Osimertinib_exp_SLC1A5,by = 'COSMIC_ID',all = T)
head(cor_df)
| COSMIC_ID | LN_IC50 | AUC | SLC1A5 | |
|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | 687777 | 0.105057 | 0.827993 | 7.738085 |
| 2 | 687798 | 0.196595 | 0.868151 | 7.311690 |
| 3 | 687799 | -0.701592 | 0.800871 | 6.404932 |
| 4 | 687800 | 0.407872 | 0.844857 | 7.837235 |
| 5 | 687802 | 1.111300 | 0.861017 | 6.296948 |
| 6 | 687807 | 3.462617 | 0.960185 | 5.368187 |
In [442]:
cor.test(cor_df$LN_IC50,cor_df$SLC1A5)
Pearson's product-moment correlation
data: cor_df$LN_IC50 and cor_df$SLC1A5
t = 0.77573, df = 59, p-value = 0.441
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.1552687 0.3436072
sample estimates:
cor
0.1004808
In [445]:
drivers$V1
- 'CD24'
- 'GTF2B'
- 'MSH6'
- 'TFDP1'
- 'MYO1B'
- 'TUBA1B'
- 'SLC1A5'
- 'TGM2'
- 'BARD1'
- 'TUBB4B'
- 'RRBP1'
- 'ANXA2'
- 'PLK2'
- 'PSMC4'
- 'DGKE'
- 'CEBPD'
- 'HSPA2'
- 'RAB31'
- 'TFAP2C'
- 'CD9'
- 'TPM1'
- 'LMO7'
- 'KLF5'
- 'TRIB1'
- 'HIST1H1C'
- 'DAAM1'
In [454]:
luad_Osimertinib_exp_m <- luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS %in% drivers$V1) %>% as.data.frame()
In [455]:
rn <- luad_Osimertinib_exp_m$GENE_SYMBOLS
luad_Osimertinib_exp_m <- luad_Osimertinib_exp_m[,2:ncol(luad_Osimertinib_exp_m)]
rownames(luad_Osimertinib_exp_m) <- rn
colnames(luad_Osimertinib_exp_m) <- substring(colnames(luad_Osimertinib_exp_m),6)
luad_Osimertinib_exp_m
| 908460 | 908465 | 910399 | 687816 | 724873 | 722045 | 724868 | 724866 | 687798 | 908475 | ⋯ | 687819 | 905942 | 905944 | 924244 | 713869 | 1298347 | 1298537 | 687777 | 724834 | 722046 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| CD9 | 11.291315 | 10.419700 | 7.617385 | 9.339332 | 9.486623 | 10.159039 | 9.754571 | 9.897625 | 11.000317 | 9.237512 | ⋯ | 10.185477 | 10.423113 | 8.428533 | 9.664688 | 3.054807 | 10.414355 | 10.662433 | 10.307319 | 9.547407 | 9.299587 |
| PSMC4 | 7.673976 | 8.210058 | 9.738164 | 9.017231 | 9.866086 | 9.256839 | 8.236600 | 8.593390 | 9.076201 | 8.868797 | ⋯ | 9.592510 | 8.500771 | 8.494822 | 7.370401 | 9.345085 | 8.092143 | 8.476768 | 9.629509 | 8.267387 | 9.203571 |
| TFAP2C | 5.569467 | 6.258649 | 7.039517 | 3.848033 | 3.362454 | 7.919826 | 3.195641 | 7.507546 | 8.096261 | 3.253618 | ⋯ | 7.981333 | 7.793775 | 3.450696 | 5.383740 | 3.483883 | 5.962629 | 5.636805 | 3.938102 | 4.795427 | 4.777092 |
| DAAM1 | 8.114960 | 9.206229 | 5.941011 | 7.709771 | 7.900853 | 6.872078 | 8.935788 | 6.802538 | 6.726673 | 8.064515 | ⋯ | 6.894659 | 7.088879 | 6.172727 | 8.090520 | 6.445547 | 7.522478 | 9.375249 | 7.165313 | 7.367304 | 7.963315 |
| KLF5 | 7.832294 | 7.489241 | 7.796212 | 7.341222 | 6.370645 | 6.072266 | 6.516501 | 8.344255 | 7.477998 | 5.895060 | ⋯ | 7.019319 | 3.754168 | 7.242407 | 6.296828 | 3.695600 | 8.726176 | 8.450361 | 8.542335 | 5.914849 | 8.988621 |
| SLC1A5 | 7.975696 | 8.479559 | 7.921446 | 8.292501 | 8.027455 | 6.631435 | 7.023257 | 6.888380 | 7.311690 | 7.524080 | ⋯ | 7.360163 | 5.701877 | 5.595287 | 6.619834 | 7.869154 | 9.138640 | 8.486290 | 7.738085 | 7.078411 | 7.740654 |
| MSH6 | 8.630002 | 7.699626 | 8.006713 | 8.336342 | 8.004477 | 8.699609 | 7.327987 | 8.493030 | 8.865416 | 8.122833 | ⋯ | 8.120081 | 9.769026 | 9.210357 | 8.789045 | 9.007159 | 7.409149 | 8.381671 | 7.572955 | 8.435810 | 8.407667 |
| TUBA1B | 11.652470 | 9.280503 | 11.567419 | 10.968004 | 11.105882 | 11.762470 | 11.085405 | 11.917632 | 12.154391 | 11.809011 | ⋯ | 11.627489 | 12.155893 | 11.650333 | 11.719388 | 12.286475 | 11.867617 | 10.931150 | 11.128865 | 12.156900 | 11.877029 |
| RRBP1 | 5.093703 | 5.621516 | 4.949829 | 4.740624 | 4.239049 | 4.460471 | 3.848750 | 5.200245 | 4.658931 | 4.943790 | ⋯ | 4.630746 | 4.579190 | 4.910837 | 5.145626 | 4.825844 | 6.193587 | 5.968903 | 3.744571 | 4.843388 | 5.645256 |
| HSPA2 | 4.022142 | 4.673292 | 10.379341 | 4.066288 | 5.921168 | 6.073845 | 7.513982 | 10.919948 | 8.445002 | 7.040063 | ⋯ | 7.647243 | 7.428458 | 10.218470 | 7.156202 | 5.791837 | 9.642464 | 7.133963 | 5.889257 | 5.148111 | 3.880645 |
| MYO1B | 7.693551 | 6.934147 | 7.422530 | 8.281631 | 6.998405 | 6.912161 | 7.326733 | 7.326659 | 6.703696 | 8.078448 | ⋯ | 6.951107 | 6.352062 | 5.894674 | 7.733862 | 7.608904 | 7.526577 | 7.177604 | 7.496097 | 7.186138 | 7.093325 |
| LMO7 | 7.491591 | 5.415113 | 3.924802 | 5.520817 | 3.930904 | 4.342913 | 4.317776 | 5.079803 | 5.031783 | 4.513817 | ⋯ | 6.624069 | 4.272535 | 4.007376 | 4.386634 | 4.241762 | 7.108192 | 5.889982 | 5.100766 | 6.435389 | 6.279339 |
| GTF2B | 9.159144 | 9.220782 | 10.071285 | 8.866781 | 9.923957 | 8.681695 | 8.387188 | 8.825132 | 9.251520 | 8.645145 | ⋯ | 8.787976 | 8.735390 | 9.410757 | 8.181802 | 9.404122 | 9.204595 | 10.618004 | 9.514425 | 9.579701 | 8.800826 |
| BARD1 | 8.495212 | 6.036531 | 5.846044 | 7.417529 | 6.664647 | 7.405108 | 6.711966 | 6.938701 | 8.135322 | 7.711713 | ⋯ | 7.936489 | 7.773346 | 7.973820 | 6.936609 | 8.045076 | 7.216246 | 6.439428 | 7.231769 | 7.870592 | 6.810523 |
| TPM1 | 3.488745 | 5.144114 | 5.528843 | 5.493340 | 5.479033 | 6.784133 | 7.232806 | 4.886198 | 6.531786 | 5.729551 | ⋯ | 7.338494 | 5.081423 | 4.438690 | 4.934804 | 6.322667 | 5.110684 | 6.116312 | 7.375009 | 4.395875 | 4.419528 |
| PLK2 | 3.198873 | 5.233384 | 4.314030 | 8.687386 | 7.571510 | 4.293385 | 7.589598 | 4.043542 | 7.927601 | 7.650513 | ⋯ | 9.155004 | 6.426634 | 4.346806 | 6.159305 | 3.512329 | 5.420284 | 7.297776 | 7.629353 | 5.117695 | 9.273644 |
| DGKE | 3.390216 | 3.243297 | 3.148960 | 3.519932 | 5.498992 | 3.095181 | 3.444873 | 3.179329 | 3.119441 | 3.204577 | ⋯ | 3.239207 | 3.301899 | 3.712990 | 3.515313 | 3.298652 | 3.097865 | 3.271151 | 3.005818 | 3.353095 | 2.997577 |
| RAB31 | 7.970076 | 8.949247 | 9.955598 | 8.762328 | 9.385821 | 9.011549 | 9.033509 | 9.424291 | 7.169477 | 8.582742 | ⋯ | 6.598109 | 8.833973 | 8.376817 | 9.485289 | 4.429509 | 6.871682 | 9.847115 | 4.637205 | 8.497761 | 7.541860 |
| TRIB1 | 5.349890 | 6.394922 | 4.632025 | 5.233623 | 4.968013 | 4.966839 | 4.751152 | 4.890243 | 5.254052 | 4.736097 | ⋯ | 4.490214 | 4.859319 | 4.168180 | 4.856539 | 4.728251 | 6.341722 | 6.287415 | 5.914905 | 5.852942 | 4.185141 |
| ANXA2 | 8.757711 | 9.140393 | 8.970238 | 9.293531 | 8.209080 | 9.130158 | 9.035340 | 8.989978 | 9.314676 | 9.206791 | ⋯ | 9.283704 | 8.522241 | 9.119012 | 8.895494 | 6.850252 | 9.625852 | 9.081707 | 9.910711 | 9.161867 | 9.460952 |
| HIST1H1C | 7.706364 | 9.011878 | 7.595978 | 6.496804 | 10.203426 | 9.261390 | 8.609759 | 10.055761 | 10.640221 | 9.178792 | ⋯ | 9.618030 | 8.405902 | 9.696507 | 8.319849 | 8.119829 | 9.576003 | 7.652675 | 7.210820 | 7.471382 | 7.422120 |
| TUBB4B | 11.259558 | 9.925173 | 11.166633 | 11.461633 | 10.180316 | 10.936143 | 10.819810 | 11.554635 | 11.722297 | 11.514311 | ⋯ | 11.708739 | 11.581694 | 11.363819 | 11.339945 | 10.570541 | 11.516563 | 10.772576 | 11.739131 | 11.800229 | 11.723652 |
| TFDP1 | 6.363628 | 5.948515 | 5.209356 | 6.479746 | 5.600078 | 6.208124 | 5.127839 | 5.290525 | 7.539822 | 6.674423 | ⋯ | 6.781304 | 6.916258 | 5.360754 | 6.338752 | 6.107802 | 7.459940 | 6.256500 | 5.727986 | 7.008906 | 7.351332 |
| TGM2 | 5.105010 | 7.035925 | 4.670315 | 4.373653 | 5.652386 | 7.282645 | 8.002773 | 3.582830 | 3.095883 | 4.511730 | ⋯ | 3.291883 | 4.559904 | 3.192086 | 4.859736 | 3.001048 | 6.390467 | 4.424562 | 6.663357 | 6.086876 | 6.428582 |
| CEBPD | 10.799592 | 10.320289 | 9.940489 | 9.430929 | 8.966978 | 9.323184 | 9.521142 | 10.681514 | 8.987169 | 9.921542 | ⋯ | 9.619719 | 8.018189 | 9.866371 | 9.145795 | 7.174265 | 9.993356 | 7.737602 | 8.457290 | 9.924065 | 9.609132 |
In [473]:
ComplexHeatmap::Heatmap(luad_Osimertinib_exp_m)
Warning message: “The input is a data frame-like object, convert it to a matrix.”
In [485]:
luad_Osimertinib_exp_TGM2 <- luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS == 'TGM2') %>% t() %>% as.data.frame()
luad_Osimertinib_exp_TGM2 <- luad_Osimertinib_exp_TGM2 %>% mutate(COSMIC_ID = substring(rownames(luad_Osimertinib_exp_TGM2),6))
luad_Osimertinib_exp_TGM2 <- luad_Osimertinib_exp_TGM2[2:nrow(luad_Osimertinib_exp_TGM2),] %>% apply(2,as.numeric)
colnames(luad_Osimertinib_exp_TGM2) <- c('TGM2','COSMIC_ID')
cor_df <- luad_Osimertinib %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>%
merge(luad_Osimertinib_exp_TGM2,by = 'COSMIC_ID',all = T)
head(cor_df)
cor.test(cor_df$LN_IC50,cor_df$TGM2)
cor.test(cor_df$AUC,cor_df$TGM2)
| COSMIC_ID | LN_IC50 | AUC | TGM2 | |
|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | |
| 1 | 687777 | 0.105057 | 0.827993 | 6.663357 |
| 2 | 687798 | 0.196595 | 0.868151 | 3.095883 |
| 3 | 687799 | -0.701592 | 0.800871 | 7.434148 |
| 4 | 687800 | 0.407872 | 0.844857 | 6.275204 |
| 5 | 687802 | 1.111300 | 0.861017 | 6.313773 |
| 6 | 687807 | 3.462617 | 0.960185 | 3.469466 |
Pearson's product-moment correlation
data: cor_df$LN_IC50 and cor_df$TGM2
t = -0.11523, df = 59, p-value = 0.9087
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.2658161 0.2377193
sample estimates:
cor
-0.01499935
Pearson's product-moment correlation
data: cor_df$AUC and cor_df$TGM2
t = -0.4178, df = 59, p-value = 0.6776
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.3020029 0.2002469
sample estimates:
cor
-0.05431266
In [481]:
dri10 <- drivers %>% dplyr::filter(counts == 10)
In [482]:
luad_Osimertinib_exp_m <- luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS %in% dri10$V1) %>% as.data.frame()
In [483]:
rn <- luad_Osimertinib_exp_m$GENE_SYMBOLS
luad_Osimertinib_exp_m <- luad_Osimertinib_exp_m[,2:ncol(luad_Osimertinib_exp_m)]
rownames(luad_Osimertinib_exp_m) <- rn
colnames(luad_Osimertinib_exp_m) <- substring(colnames(luad_Osimertinib_exp_m),6)
luad_Osimertinib_exp_m
| 908460 | 908465 | 910399 | 687816 | 724873 | 722045 | 724868 | 724866 | 687798 | 908475 | ⋯ | 687819 | 905942 | 905944 | 924244 | 713869 | 1298347 | 1298537 | 687777 | 724834 | 722046 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| SLC1A5 | 7.975696 | 8.479559 | 7.921446 | 8.292501 | 8.027455 | 6.631435 | 7.023257 | 6.888380 | 7.311690 | 7.524080 | ⋯ | 7.360163 | 5.701877 | 5.595287 | 6.619834 | 7.869154 | 9.138640 | 8.486290 | 7.738085 | 7.078411 | 7.740654 |
| MSH6 | 8.630002 | 7.699626 | 8.006713 | 8.336342 | 8.004477 | 8.699609 | 7.327987 | 8.493030 | 8.865416 | 8.122833 | ⋯ | 8.120081 | 9.769026 | 9.210357 | 8.789045 | 9.007159 | 7.409149 | 8.381671 | 7.572955 | 8.435810 | 8.407667 |
| TUBA1B | 11.652470 | 9.280503 | 11.567419 | 10.968004 | 11.105882 | 11.762470 | 11.085405 | 11.917632 | 12.154391 | 11.809011 | ⋯ | 11.627489 | 12.155893 | 11.650333 | 11.719388 | 12.286475 | 11.867617 | 10.931150 | 11.128865 | 12.156900 | 11.877029 |
| MYO1B | 7.693551 | 6.934147 | 7.422530 | 8.281631 | 6.998405 | 6.912161 | 7.326733 | 7.326659 | 6.703696 | 8.078448 | ⋯ | 6.951107 | 6.352062 | 5.894674 | 7.733862 | 7.608904 | 7.526577 | 7.177604 | 7.496097 | 7.186138 | 7.093325 |
| GTF2B | 9.159144 | 9.220782 | 10.071285 | 8.866781 | 9.923957 | 8.681695 | 8.387188 | 8.825132 | 9.251520 | 8.645145 | ⋯ | 8.787976 | 8.735390 | 9.410757 | 8.181802 | 9.404122 | 9.204595 | 10.618004 | 9.514425 | 9.579701 | 8.800826 |
| BARD1 | 8.495212 | 6.036531 | 5.846044 | 7.417529 | 6.664647 | 7.405108 | 6.711966 | 6.938701 | 8.135322 | 7.711713 | ⋯ | 7.936489 | 7.773346 | 7.973820 | 6.936609 | 8.045076 | 7.216246 | 6.439428 | 7.231769 | 7.870592 | 6.810523 |
| TUBB4B | 11.259558 | 9.925173 | 11.166633 | 11.461633 | 10.180316 | 10.936143 | 10.819810 | 11.554635 | 11.722297 | 11.514311 | ⋯ | 11.708739 | 11.581694 | 11.363819 | 11.339945 | 10.570541 | 11.516563 | 10.772576 | 11.739131 | 11.800229 | 11.723652 |
| TFDP1 | 6.363628 | 5.948515 | 5.209356 | 6.479746 | 5.600078 | 6.208124 | 5.127839 | 5.290525 | 7.539822 | 6.674423 | ⋯ | 6.781304 | 6.916258 | 5.360754 | 6.338752 | 6.107802 | 7.459940 | 6.256500 | 5.727986 | 7.008906 | 7.351332 |
| TGM2 | 5.105010 | 7.035925 | 4.670315 | 4.373653 | 5.652386 | 7.282645 | 8.002773 | 3.582830 | 3.095883 | 4.511730 | ⋯ | 3.291883 | 4.559904 | 3.192086 | 4.859736 | 3.001048 | 6.390467 | 4.424562 | 6.663357 | 6.086876 | 6.428582 |
In [484]:
ComplexHeatmap::Heatmap(luad_Osimertinib_exp_m)
Warning message: “The input is a data frame-like object, convert it to a matrix.”
In [486]:
Osimertinib <- GDSC_drug2 %>% dplyr::filter(GDSC_drug2$COSMIC_ID %in% substring(colnames(GDSC_exp),6)) %>%
dplyr::filter(DRUG_NAME == 'Osimertinib')
Osimertinib
| DATASET | NLME_RESULT_ID | NLME_CURVE_ID | COSMIC_ID | CELL_LINE_NAME | SANGER_MODEL_ID | TCGA_DESC | DRUG_ID | DRUG_NAME | PUTATIVE_TARGET | PATHWAY_NAME | COMPANY_ID | WEBRELEASE | MIN_CONC | MAX_CONC | LN_IC50 | AUC | RMSE | Z_SCORE |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| GDSC2 | 343 | 15946501 | 683667 | PFSK-1 | SIDM01132 | MB | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.279876 | 0.948851 | 0.083039 | -0.290963 |
| GDSC2 | 343 | 15946766 | 684052 | A673 | SIDM00848 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.372555 | 0.955487 | 0.075005 | -0.218362 |
| GDSC2 | 343 | 15947052 | 684057 | ES5 | SIDM00263 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.167834 | 0.947725 | 0.057817 | -0.378733 |
| GDSC2 | 343 | 15947305 | 684059 | ES7 | SIDM00269 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.865174 | 0.969978 | 0.024503 | 0.167538 |
| GDSC2 | 343 | 15947587 | 684062 | EW-11 | SIDM00203 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.335056 | 0.934942 | 0.039643 | -0.247737 |
| GDSC2 | 343 | 15947867 | 684072 | SK-ES-1 | SIDM01111 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.658029 | 0.972790 | 0.076845 | 0.005268 |
| GDSC2 | 343 | 15948147 | 687448 | COLO-829 | SIDM00909 | SKCM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.918938 | 0.938208 | 0.056445 | 0.993017 |
| GDSC2 | 343 | 15948426 | 687452 | 5637 | SIDM00807 | BLCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.457485 | 0.888955 | 0.070200 | -0.935194 |
| GDSC2 | 343 | 15948707 | 687455 | RT4 | SIDM01085 | BLCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.784537 | 0.981413 | 0.067026 | 0.887732 |
| GDSC2 | 343 | 15948988 | 687457 | SW780 | SIDM01160 | BLCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.301737 | 0.958979 | 0.058973 | 0.509525 |
| GDSC2 | 343 | 15949269 | 687459 | TCCSUP | SIDM01190 | BLCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.756258 | 0.965290 | 0.044146 | 0.865579 |
| GDSC2 | 343 | 15949552 | 687505 | C-33-A | SIDM00889 | CESC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.189240 | 0.883480 | 0.063777 | -1.145327 |
| GDSC2 | 343 | 15949831 | 687506 | C-4-I | SIDM00905 | CESC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.276588 | 0.906473 | 0.040211 | -1.076901 |
| GDSC2 | 343 | 15950113 | 687514 | ME-180 | SIDM00627 | CESC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.037413 | 0.860357 | 0.026376 | -1.322878 |
| GDSC2 | 343 | 15950393 | 687561 | 42-MG-BA | SIDM00982 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.475281 | 0.949391 | 0.042978 | -0.137890 |
| GDSC2 | 343 | 15950673 | 687562 | 8-MG-BA | SIDM00998 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.368088 | 0.982457 | 0.082000 | 0.561502 |
| GDSC2 | 343 | 15950954 | 687563 | A172 | SIDM00799 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.588013 | 0.942300 | 0.081864 | -0.049580 |
| GDSC2 | 343 | 15951235 | 687568 | GB-1 | SIDM00581 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.952566 | 0.975561 | 0.104329 | 1.802722 |
| GDSC2 | 343 | 15951518 | 687586 | T98G | SIDM01171 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.869108 | 0.981281 | 0.019718 | 0.953982 |
| GDSC2 | 343 | 15951798 | 687588 | U-118-MG | SIDM01193 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.796025 | 0.926795 | 0.059708 | 0.113368 |
| GDSC2 | 343 | 15952059 | 687590 | U-87-MG | SIDM01189 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.304401 | 0.958807 | 0.034969 | 0.511612 |
| GDSC2 | 343 | 15952260 | 687592 | YKG-1 | SIDM00315 | GBM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.641181 | 0.946104 | 0.080434 | -0.007930 |
| GDSC2 | 343 | 15952440 | 687596 | ChaGo-K-1 | SIDM00924 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.041674 | 0.969724 | 0.126241 | 0.305801 |
| GDSC2 | 343 | 15952683 | 687600 | NCI-H720 | SIDM01120 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.271699 | 0.976071 | 0.116991 | 0.485994 |
| GDSC2 | 343 | 15952962 | 687777 | Calu-3 | SIDM00922 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.105057 | 0.827993 | 0.098212 | -1.211272 |
| GDSC2 | 343 | 15953242 | 687780 | COR-L23 | SIDM00512 | UNCLASSIFIED | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.272615 | 0.954236 | 0.060843 | 0.486711 |
| GDSC2 | 343 | 15953524 | 687787 | LK-2 | SIDM00548 | LUSC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.440981 | 0.935244 | 0.065657 | -0.164759 |
| GDSC2 | 343 | 15953984 | 687798 | NCI-H1623 | SIDM00747 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.196595 | 0.868151 | 0.052729 | -1.139565 |
| GDSC2 | 343 | 15954155 | 687799 | NCI-H1648 | SIDM00746 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.701592 | 0.800871 | 0.093298 | -1.843171 |
| GDSC2 | 343 | 15954434 | 687800 | NCI-H1650 | SIDM00745 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.407872 | 0.844857 | 0.134400 | -0.974058 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| GDSC2 | 343 | 16182144 | 1480372 | PEO1 | SIDM00472 | OV | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.750945 | 0.949725 | 0.032682 | 0.861418 |
| GDSC2 | 343 | 16182353 | 1480374 | UWB1.289 | SIDM00815 | OV | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.236974 | 0.863054 | 0.068164 | -1.107934 |
| GDSC2 | 343 | 16182606 | 1503361 | FLO-1 | SIDM01041 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.859503 | 0.945865 | 0.059545 | 0.163096 |
| GDSC2 | 343 | 16182886 | 1503362 | OACp4C | SIDM00445 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.822752 | 0.956546 | 0.075153 | 0.134306 |
| GDSC2 | 343 | 16183167 | 1503363 | OACM5-1 | SIDM00444 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.211594 | 0.974548 | 0.028010 | 1.222273 |
| GDSC2 | 343 | 16183448 | 1503364 | SK-GT-2 | SIDM00393 | STAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -3.066920 | 0.548453 | 0.117406 | -3.696080 |
| GDSC2 | 343 | 16183730 | 1503365 | SK-GT-4 | SIDM00483 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.863469 | 0.882733 | 0.073594 | 0.166202 |
| GDSC2 | 343 | 16184009 | 1503366 | ESO26 | SIDM00539 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.239166 | 0.934399 | 0.042767 | -0.322854 |
| GDSC2 | 343 | 16184290 | 1503367 | ESO51 | SIDM00538 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 3.157952 | 0.975412 | 0.024334 | 1.180251 |
| GDSC2 | 343 | 16184573 | 1503368 | KYAE-1 | SIDM00530 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -1.560172 | 0.739831 | 0.039288 | -2.515751 |
| GDSC2 | 343 | 16184782 | 1503369 | EMC-BAC-1 | SIDM00048 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.042516 | 0.856887 | 0.132649 | -1.326876 |
| GDSC2 | 343 | 16185032 | 1503370 | EMC-BAC-2 | SIDM00047 | LUAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.664314 | 0.886320 | 0.090450 | -0.773171 |
| GDSC2 | 343 | 16185313 | 1503371 | TE-4 | SIDM00250 | ESCA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -1.707318 | 0.723921 | 0.058785 | -2.631019 |
| GDSC2 | 343 | 16185779 | 1509073 | NCC010 | SIDM00231 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.574713 | 0.921736 | 0.083888 | -0.843361 |
| GDSC2 | 343 | 16185959 | 1509074 | NCC021 | SIDM00232 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.338782 | 0.953848 | 0.078305 | 0.538544 |
| GDSC2 | 343 | 16186139 | 1524414 | RCC-FG2 | SIDM00819 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.846549 | 0.911654 | 0.134748 | -0.630415 |
| GDSC2 | 343 | 16186319 | 1524415 | RCC-JF | SIDM00818 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.926748 | 0.923610 | 0.066450 | -0.567591 |
| GDSC2 | 343 | 16186571 | 1524416 | RCC-JW | SIDM00817 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.237197 | 0.954133 | 0.037277 | 0.458967 |
| GDSC2 | 343 | 16186780 | 1524417 | RCC-ER | SIDM00820 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.680820 | 0.956640 | 0.019253 | 0.023121 |
| GDSC2 | 343 | 16186960 | 1524418 | RCC-AB | SIDM00821 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.856423 | 0.888532 | 0.088464 | -0.622681 |
| GDSC2 | 343 | 16187140 | 1524419 | RCC-MF | SIDM00816 | KIRC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.965417 | 0.964731 | 0.062086 | 0.246064 |
| GDSC2 | 343 | 16187320 | 1659817 | KMS-11 | SIDM00608 | MM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.576469 | 0.967225 | 0.059140 | -0.058623 |
| GDSC2 | 343 | 16187570 | 1659818 | MM1S | SIDM01265 | MM | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.862097 | 0.967879 | 0.008336 | -0.618235 |
| GDSC2 | 343 | 16187776 | 1659819 | OCI-LY7 | SIDM00459 | DLBC | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.948492 | 0.975369 | 0.019785 | -0.550557 |
| GDSC2 | 343 | 16188178 | 1659928 | SNU-175 | SIDM00216 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | -0.211949 | 0.857902 | 0.056394 | -1.459603 |
| GDSC2 | 343 | 16188388 | 1659929 | SNU-283 | SIDM00215 | NA | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 0.619565 | 0.913037 | 0.081642 | -0.808226 |
| GDSC2 | 343 | 16188640 | 1660034 | SNU-407 | SIDM00214 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.115710 | 0.919957 | 0.050110 | -0.419564 |
| GDSC2 | 343 | 16188910 | 1660035 | SNU-61 | SIDM00194 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.378973 | 0.964479 | 0.044681 | 0.570028 |
| GDSC2 | 343 | 16189177 | 1660036 | SNU-81 | SIDM00193 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 1.891271 | 0.975764 | 0.057990 | 0.187981 |
| GDSC2 | 343 | 16189429 | 1674021 | SNU-C5 | SIDM00498 | COREAD | 1919 | Osimertinib | EGFR | EGFR signaling | 1046 | Y | 0.001001 | 1 | 2.503527 | 0.957638 | 0.057810 | 0.667600 |
In [681]:
ComplexHeatmap::Heatmap(normalize(luad_Osimertinib[,c('LN_IC50')]))
In [683]:
ComplexHeatmap::Heatmap(normalize(Osimertinib[,c('LN_IC50')]))
In [682]:
ComplexHeatmap::Heatmap(normalize(luad_Osimertinib[,c('AUC')]))
In [684]:
ComplexHeatmap::Heatmap(normalize(Osimertinib[,c('AUC')]))
In [ ]:
用权重加权基因算得分 只看正 只看负 加载一起
In [ ]:
和药物反应相关高的基因与driver找关联
In [ ]:
提高driver范围,在所以癌症中做
In [ ]:
跨组学去找CNV
In [ ]:
In [17]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_sen_res'
read_dir <- file.path(read_dir,run_name)
In [18]:
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
head(drivers)
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| CD24 | 0.0008392735 | 0.0003266406 | -5.656427e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| GTF2B | 0.0010324697 | 0.0003080479 | -1.316672e-04 | 10 | TRUE | FALSE | FALSE | FALSE |
| MSH6 | 0.0016612993 | 0.0004270973 | 7.425508e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| TFDP1 | 0.0013715578 | 0.0005694479 | -5.785423e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| MYO1B | 0.0010195644 | 0.0002886838 | 1.748662e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TUBA1B | 0.0018392079 | 0.0003183509 | -1.412048e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
In [19]:
drivers
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| CD24 | 0.0008392735 | 3.266406e-04 | -5.656427e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| GTF2B | 0.0010324697 | 3.080479e-04 | -1.316672e-04 | 10 | TRUE | FALSE | FALSE | FALSE |
| MSH6 | 0.0016612993 | 4.270973e-04 | 7.425508e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| TFDP1 | 0.0013715578 | 5.694479e-04 | -5.785423e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| MYO1B | 0.0010195644 | 2.886838e-04 | 1.748662e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TUBA1B | 0.0018392079 | 3.183509e-04 | -1.412048e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| SLC1A5 | 0.0021976048 | 6.430204e-04 | 1.082030e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TGM2 | 0.0007415661 | 2.308803e-04 | 5.989809e-06 | 10 | FALSE | FALSE | FALSE | FALSE |
| BARD1 | 0.0021673360 | 6.107930e-04 | 1.084314e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TUBB4B | 0.0006375848 | 2.468592e-04 | 1.148352e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| RRBP1 | 0.0006809303 | 2.972012e-04 | -1.221697e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| ANXA2 | 0.0013427509 | 6.148865e-04 | -7.150776e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| PLK2 | 0.0011322402 | 5.536364e-04 | 6.720297e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| PSMC4 | 0.0009214443 | 3.828258e-04 | -4.451585e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| DGKE | 0.0009730622 | 2.876285e-04 | -5.831443e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| CEBPD | 0.0006121597 | 2.758779e-04 | 7.729752e-05 | 9 | TRUE | FALSE | FALSE | FALSE |
| HSPA2 | 0.0006995864 | 2.921626e-04 | -2.506919e-04 | 8 | FALSE | FALSE | FALSE | FALSE |
| RAB31 | 0.0011450001 | 3.613637e-04 | -5.959134e-05 | 8 | FALSE | FALSE | FALSE | FALSE |
| TFAP2C | 0.0002400025 | 8.345076e-05 | 1.130995e-05 | 7 | TRUE | FALSE | FALSE | FALSE |
| CD9 | 0.0020897718 | 4.177533e-04 | -1.463574e-04 | 6 | FALSE | FALSE | FALSE | FALSE |
| TPM1 | 0.0003337573 | 1.718587e-04 | 2.991494e-05 | 6 | FALSE | FALSE | FALSE | FALSE |
| LMO7 | 0.0006043960 | 2.033280e-04 | 2.352714e-04 | 6 | FALSE | FALSE | FALSE | FALSE |
| KLF5 | 0.0003624802 | 2.617805e-04 | 1.071805e-04 | 6 | TRUE | FALSE | FALSE | FALSE |
| TRIB1 | 0.0004753006 | 2.120645e-04 | 2.118026e-05 | 6 | FALSE | FALSE | FALSE | FALSE |
| HIST1H1C | 0.0003803697 | 1.031536e-04 | -7.409843e-05 | 4 | FALSE | FALSE | FALSE | FALSE |
| DAAM1 | 0.0020700805 | 4.885557e-04 | 1.041791e-04 | 4 | FALSE | FALSE | FALSE | FALSE |
In [20]:
luad_Osimertinib_exp_m <- luad_Osimertinib_exp%>% dplyr::filter(GENE_SYMBOLS %in% drivers$V1) %>% as.data.frame()
In [21]:
rn <- luad_Osimertinib_exp_m$GENE_SYMBOLS
luad_Osimertinib_exp_m <- luad_Osimertinib_exp_m[,2:ncol(luad_Osimertinib_exp_m)]
rownames(luad_Osimertinib_exp_m) <- rn
colnames(luad_Osimertinib_exp_m) <- substring(colnames(luad_Osimertinib_exp_m),6)
luad_Osimertinib_exp_m
| 908460 | 908465 | 910399 | 687816 | 724873 | 722045 | 724868 | 724866 | 687798 | 908475 | ⋯ | 687819 | 905942 | 905944 | 924244 | 713869 | 1298347 | 1298537 | 687777 | 724834 | 722046 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| CD9 | 11.291315 | 10.419700 | 7.617385 | 9.339332 | 9.486623 | 10.159039 | 9.754571 | 9.897625 | 11.000317 | 9.237512 | ⋯ | 10.185477 | 10.423113 | 8.428533 | 9.664688 | 3.054807 | 10.414355 | 10.662433 | 10.307319 | 9.547407 | 9.299587 |
| PSMC4 | 7.673976 | 8.210058 | 9.738164 | 9.017231 | 9.866086 | 9.256839 | 8.236600 | 8.593390 | 9.076201 | 8.868797 | ⋯ | 9.592510 | 8.500771 | 8.494822 | 7.370401 | 9.345085 | 8.092143 | 8.476768 | 9.629509 | 8.267387 | 9.203571 |
| TFAP2C | 5.569467 | 6.258649 | 7.039517 | 3.848033 | 3.362454 | 7.919826 | 3.195641 | 7.507546 | 8.096261 | 3.253618 | ⋯ | 7.981333 | 7.793775 | 3.450696 | 5.383740 | 3.483883 | 5.962629 | 5.636805 | 3.938102 | 4.795427 | 4.777092 |
| DAAM1 | 8.114960 | 9.206229 | 5.941011 | 7.709771 | 7.900853 | 6.872078 | 8.935788 | 6.802538 | 6.726673 | 8.064515 | ⋯ | 6.894659 | 7.088879 | 6.172727 | 8.090520 | 6.445547 | 7.522478 | 9.375249 | 7.165313 | 7.367304 | 7.963315 |
| KLF5 | 7.832294 | 7.489241 | 7.796212 | 7.341222 | 6.370645 | 6.072266 | 6.516501 | 8.344255 | 7.477998 | 5.895060 | ⋯ | 7.019319 | 3.754168 | 7.242407 | 6.296828 | 3.695600 | 8.726176 | 8.450361 | 8.542335 | 5.914849 | 8.988621 |
| SLC1A5 | 7.975696 | 8.479559 | 7.921446 | 8.292501 | 8.027455 | 6.631435 | 7.023257 | 6.888380 | 7.311690 | 7.524080 | ⋯ | 7.360163 | 5.701877 | 5.595287 | 6.619834 | 7.869154 | 9.138640 | 8.486290 | 7.738085 | 7.078411 | 7.740654 |
| MSH6 | 8.630002 | 7.699626 | 8.006713 | 8.336342 | 8.004477 | 8.699609 | 7.327987 | 8.493030 | 8.865416 | 8.122833 | ⋯ | 8.120081 | 9.769026 | 9.210357 | 8.789045 | 9.007159 | 7.409149 | 8.381671 | 7.572955 | 8.435810 | 8.407667 |
| TUBA1B | 11.652470 | 9.280503 | 11.567419 | 10.968004 | 11.105882 | 11.762470 | 11.085405 | 11.917632 | 12.154391 | 11.809011 | ⋯ | 11.627489 | 12.155893 | 11.650333 | 11.719388 | 12.286475 | 11.867617 | 10.931150 | 11.128865 | 12.156900 | 11.877029 |
| RRBP1 | 5.093703 | 5.621516 | 4.949829 | 4.740624 | 4.239049 | 4.460471 | 3.848750 | 5.200245 | 4.658931 | 4.943790 | ⋯ | 4.630746 | 4.579190 | 4.910837 | 5.145626 | 4.825844 | 6.193587 | 5.968903 | 3.744571 | 4.843388 | 5.645256 |
| HSPA2 | 4.022142 | 4.673292 | 10.379341 | 4.066288 | 5.921168 | 6.073845 | 7.513982 | 10.919948 | 8.445002 | 7.040063 | ⋯ | 7.647243 | 7.428458 | 10.218470 | 7.156202 | 5.791837 | 9.642464 | 7.133963 | 5.889257 | 5.148111 | 3.880645 |
| MYO1B | 7.693551 | 6.934147 | 7.422530 | 8.281631 | 6.998405 | 6.912161 | 7.326733 | 7.326659 | 6.703696 | 8.078448 | ⋯ | 6.951107 | 6.352062 | 5.894674 | 7.733862 | 7.608904 | 7.526577 | 7.177604 | 7.496097 | 7.186138 | 7.093325 |
| LMO7 | 7.491591 | 5.415113 | 3.924802 | 5.520817 | 3.930904 | 4.342913 | 4.317776 | 5.079803 | 5.031783 | 4.513817 | ⋯ | 6.624069 | 4.272535 | 4.007376 | 4.386634 | 4.241762 | 7.108192 | 5.889982 | 5.100766 | 6.435389 | 6.279339 |
| GTF2B | 9.159144 | 9.220782 | 10.071285 | 8.866781 | 9.923957 | 8.681695 | 8.387188 | 8.825132 | 9.251520 | 8.645145 | ⋯ | 8.787976 | 8.735390 | 9.410757 | 8.181802 | 9.404122 | 9.204595 | 10.618004 | 9.514425 | 9.579701 | 8.800826 |
| BARD1 | 8.495212 | 6.036531 | 5.846044 | 7.417529 | 6.664647 | 7.405108 | 6.711966 | 6.938701 | 8.135322 | 7.711713 | ⋯ | 7.936489 | 7.773346 | 7.973820 | 6.936609 | 8.045076 | 7.216246 | 6.439428 | 7.231769 | 7.870592 | 6.810523 |
| TPM1 | 3.488745 | 5.144114 | 5.528843 | 5.493340 | 5.479033 | 6.784133 | 7.232806 | 4.886198 | 6.531786 | 5.729551 | ⋯ | 7.338494 | 5.081423 | 4.438690 | 4.934804 | 6.322667 | 5.110684 | 6.116312 | 7.375009 | 4.395875 | 4.419528 |
| PLK2 | 3.198873 | 5.233384 | 4.314030 | 8.687386 | 7.571510 | 4.293385 | 7.589598 | 4.043542 | 7.927601 | 7.650513 | ⋯ | 9.155004 | 6.426634 | 4.346806 | 6.159305 | 3.512329 | 5.420284 | 7.297776 | 7.629353 | 5.117695 | 9.273644 |
| DGKE | 3.390216 | 3.243297 | 3.148960 | 3.519932 | 5.498992 | 3.095181 | 3.444873 | 3.179329 | 3.119441 | 3.204577 | ⋯ | 3.239207 | 3.301899 | 3.712990 | 3.515313 | 3.298652 | 3.097865 | 3.271151 | 3.005818 | 3.353095 | 2.997577 |
| RAB31 | 7.970076 | 8.949247 | 9.955598 | 8.762328 | 9.385821 | 9.011549 | 9.033509 | 9.424291 | 7.169477 | 8.582742 | ⋯ | 6.598109 | 8.833973 | 8.376817 | 9.485289 | 4.429509 | 6.871682 | 9.847115 | 4.637205 | 8.497761 | 7.541860 |
| TRIB1 | 5.349890 | 6.394922 | 4.632025 | 5.233623 | 4.968013 | 4.966839 | 4.751152 | 4.890243 | 5.254052 | 4.736097 | ⋯ | 4.490214 | 4.859319 | 4.168180 | 4.856539 | 4.728251 | 6.341722 | 6.287415 | 5.914905 | 5.852942 | 4.185141 |
| ANXA2 | 8.757711 | 9.140393 | 8.970238 | 9.293531 | 8.209080 | 9.130158 | 9.035340 | 8.989978 | 9.314676 | 9.206791 | ⋯ | 9.283704 | 8.522241 | 9.119012 | 8.895494 | 6.850252 | 9.625852 | 9.081707 | 9.910711 | 9.161867 | 9.460952 |
| HIST1H1C | 7.706364 | 9.011878 | 7.595978 | 6.496804 | 10.203426 | 9.261390 | 8.609759 | 10.055761 | 10.640221 | 9.178792 | ⋯ | 9.618030 | 8.405902 | 9.696507 | 8.319849 | 8.119829 | 9.576003 | 7.652675 | 7.210820 | 7.471382 | 7.422120 |
| TUBB4B | 11.259558 | 9.925173 | 11.166633 | 11.461633 | 10.180316 | 10.936143 | 10.819810 | 11.554635 | 11.722297 | 11.514311 | ⋯ | 11.708739 | 11.581694 | 11.363819 | 11.339945 | 10.570541 | 11.516563 | 10.772576 | 11.739131 | 11.800229 | 11.723652 |
| TFDP1 | 6.363628 | 5.948515 | 5.209356 | 6.479746 | 5.600078 | 6.208124 | 5.127839 | 5.290525 | 7.539822 | 6.674423 | ⋯ | 6.781304 | 6.916258 | 5.360754 | 6.338752 | 6.107802 | 7.459940 | 6.256500 | 5.727986 | 7.008906 | 7.351332 |
| TGM2 | 5.105010 | 7.035925 | 4.670315 | 4.373653 | 5.652386 | 7.282645 | 8.002773 | 3.582830 | 3.095883 | 4.511730 | ⋯ | 3.291883 | 4.559904 | 3.192086 | 4.859736 | 3.001048 | 6.390467 | 4.424562 | 6.663357 | 6.086876 | 6.428582 |
| CEBPD | 10.799592 | 10.320289 | 9.940489 | 9.430929 | 8.966978 | 9.323184 | 9.521142 | 10.681514 | 8.987169 | 9.921542 | ⋯ | 9.619719 | 8.018189 | 9.866371 | 9.145795 | 7.174265 | 9.993356 | 7.737602 | 8.457290 | 9.924065 | 9.609132 |
In [22]:
luad_Osimertinib_exp_m$V1 = rownames(luad_Osimertinib_exp_m)
In [23]:
score_df <- merge(luad_Osimertinib_exp_m,drivers,by = 'V1',all = F)
score_df
| V1 | 908460 | 908465 | 910399 | 687816 | 724873 | 722045 | 724868 | 724866 | 687798 | ⋯ | 724834 | 722046 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> |
| ANXA2 | 8.757711 | 9.140393 | 8.970238 | 9.293531 | 8.209080 | 9.130158 | 9.035340 | 8.989978 | 9.314676 | ⋯ | 9.161867 | 9.460952 | 0.0013427509 | 6.148865e-04 | -7.150776e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| BARD1 | 8.495212 | 6.036531 | 5.846044 | 7.417529 | 6.664647 | 7.405108 | 6.711966 | 6.938701 | 8.135322 | ⋯ | 7.870592 | 6.810523 | 0.0021673360 | 6.107930e-04 | 1.084314e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| CD9 | 11.291315 | 10.419700 | 7.617385 | 9.339332 | 9.486623 | 10.159039 | 9.754571 | 9.897625 | 11.000317 | ⋯ | 9.547407 | 9.299587 | 0.0020897718 | 4.177533e-04 | -1.463574e-04 | 6 | FALSE | FALSE | FALSE | FALSE |
| CEBPD | 10.799592 | 10.320289 | 9.940489 | 9.430929 | 8.966978 | 9.323184 | 9.521142 | 10.681514 | 8.987169 | ⋯ | 9.924065 | 9.609132 | 0.0006121597 | 2.758779e-04 | 7.729752e-05 | 9 | TRUE | FALSE | FALSE | FALSE |
| DAAM1 | 8.114960 | 9.206229 | 5.941011 | 7.709771 | 7.900853 | 6.872078 | 8.935788 | 6.802538 | 6.726673 | ⋯ | 7.367304 | 7.963315 | 0.0020700805 | 4.885557e-04 | 1.041791e-04 | 4 | FALSE | FALSE | FALSE | FALSE |
| DGKE | 3.390216 | 3.243297 | 3.148960 | 3.519932 | 5.498992 | 3.095181 | 3.444873 | 3.179329 | 3.119441 | ⋯ | 3.353095 | 2.997577 | 0.0009730622 | 2.876285e-04 | -5.831443e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| GTF2B | 9.159144 | 9.220782 | 10.071285 | 8.866781 | 9.923957 | 8.681695 | 8.387188 | 8.825132 | 9.251520 | ⋯ | 9.579701 | 8.800826 | 0.0010324697 | 3.080479e-04 | -1.316672e-04 | 10 | TRUE | FALSE | FALSE | FALSE |
| HIST1H1C | 7.706364 | 9.011878 | 7.595978 | 6.496804 | 10.203426 | 9.261390 | 8.609759 | 10.055761 | 10.640221 | ⋯ | 7.471382 | 7.422120 | 0.0003803697 | 1.031536e-04 | -7.409843e-05 | 4 | FALSE | FALSE | FALSE | FALSE |
| HSPA2 | 4.022142 | 4.673292 | 10.379341 | 4.066288 | 5.921168 | 6.073845 | 7.513982 | 10.919948 | 8.445002 | ⋯ | 5.148111 | 3.880645 | 0.0006995864 | 2.921626e-04 | -2.506919e-04 | 8 | FALSE | FALSE | FALSE | FALSE |
| KLF5 | 7.832294 | 7.489241 | 7.796212 | 7.341222 | 6.370645 | 6.072266 | 6.516501 | 8.344255 | 7.477998 | ⋯ | 5.914849 | 8.988621 | 0.0003624802 | 2.617805e-04 | 1.071805e-04 | 6 | TRUE | FALSE | FALSE | FALSE |
| LMO7 | 7.491591 | 5.415113 | 3.924802 | 5.520817 | 3.930904 | 4.342913 | 4.317776 | 5.079803 | 5.031783 | ⋯ | 6.435389 | 6.279339 | 0.0006043960 | 2.033280e-04 | 2.352714e-04 | 6 | FALSE | FALSE | FALSE | FALSE |
| MSH6 | 8.630002 | 7.699626 | 8.006713 | 8.336342 | 8.004477 | 8.699609 | 7.327987 | 8.493030 | 8.865416 | ⋯ | 8.435810 | 8.407667 | 0.0016612993 | 4.270973e-04 | 7.425508e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
| MYO1B | 7.693551 | 6.934147 | 7.422530 | 8.281631 | 6.998405 | 6.912161 | 7.326733 | 7.326659 | 6.703696 | ⋯ | 7.186138 | 7.093325 | 0.0010195644 | 2.886838e-04 | 1.748662e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| PLK2 | 3.198873 | 5.233384 | 4.314030 | 8.687386 | 7.571510 | 4.293385 | 7.589598 | 4.043542 | 7.927601 | ⋯ | 5.117695 | 9.273644 | 0.0011322402 | 5.536364e-04 | 6.720297e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| PSMC4 | 7.673976 | 8.210058 | 9.738164 | 9.017231 | 9.866086 | 9.256839 | 8.236600 | 8.593390 | 9.076201 | ⋯ | 8.267387 | 9.203571 | 0.0009214443 | 3.828258e-04 | -4.451585e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| RAB31 | 7.970076 | 8.949247 | 9.955598 | 8.762328 | 9.385821 | 9.011549 | 9.033509 | 9.424291 | 7.169477 | ⋯ | 8.497761 | 7.541860 | 0.0011450001 | 3.613637e-04 | -5.959134e-05 | 8 | FALSE | FALSE | FALSE | FALSE |
| RRBP1 | 5.093703 | 5.621516 | 4.949829 | 4.740624 | 4.239049 | 4.460471 | 3.848750 | 5.200245 | 4.658931 | ⋯ | 4.843388 | 5.645256 | 0.0006809303 | 2.972012e-04 | -1.221697e-05 | 9 | FALSE | FALSE | FALSE | FALSE |
| SLC1A5 | 7.975696 | 8.479559 | 7.921446 | 8.292501 | 8.027455 | 6.631435 | 7.023257 | 6.888380 | 7.311690 | ⋯ | 7.078411 | 7.740654 | 0.0021976048 | 6.430204e-04 | 1.082030e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TFAP2C | 5.569467 | 6.258649 | 7.039517 | 3.848033 | 3.362454 | 7.919826 | 3.195641 | 7.507546 | 8.096261 | ⋯ | 4.795427 | 4.777092 | 0.0002400025 | 8.345076e-05 | 1.130995e-05 | 7 | TRUE | FALSE | FALSE | FALSE |
| TFDP1 | 6.363628 | 5.948515 | 5.209356 | 6.479746 | 5.600078 | 6.208124 | 5.127839 | 5.290525 | 7.539822 | ⋯ | 7.008906 | 7.351332 | 0.0013715578 | 5.694479e-04 | -5.785423e-05 | 10 | TRUE | FALSE | FALSE | FALSE |
| TGM2 | 5.105010 | 7.035925 | 4.670315 | 4.373653 | 5.652386 | 7.282645 | 8.002773 | 3.582830 | 3.095883 | ⋯ | 6.086876 | 6.428582 | 0.0007415661 | 2.308803e-04 | 5.989809e-06 | 10 | FALSE | FALSE | FALSE | FALSE |
| TPM1 | 3.488745 | 5.144114 | 5.528843 | 5.493340 | 5.479033 | 6.784133 | 7.232806 | 4.886198 | 6.531786 | ⋯ | 4.395875 | 4.419528 | 0.0003337573 | 1.718587e-04 | 2.991494e-05 | 6 | FALSE | FALSE | FALSE | FALSE |
| TRIB1 | 5.349890 | 6.394922 | 4.632025 | 5.233623 | 4.968013 | 4.966839 | 4.751152 | 4.890243 | 5.254052 | ⋯ | 5.852942 | 4.185141 | 0.0004753006 | 2.120645e-04 | 2.118026e-05 | 6 | FALSE | FALSE | FALSE | FALSE |
| TUBA1B | 11.652470 | 9.280503 | 11.567419 | 10.968004 | 11.105882 | 11.762470 | 11.085405 | 11.917632 | 12.154391 | ⋯ | 12.156900 | 11.877029 | 0.0018392079 | 3.183509e-04 | -1.412048e-04 | 10 | FALSE | FALSE | FALSE | FALSE |
| TUBB4B | 11.259558 | 9.925173 | 11.166633 | 11.461633 | 10.180316 | 10.936143 | 10.819810 | 11.554635 | 11.722297 | ⋯ | 11.800229 | 11.723652 | 0.0006375848 | 2.468592e-04 | 1.148352e-05 | 10 | FALSE | FALSE | FALSE | FALSE |
In [31]:
FUN1 <- function(x){
sum(x*score_df$weight_shap_total_mean)
#sum(x)
}
score <- apply(score_df[,2:61],MARGIN = 2,FUN = FUN1)
In [32]:
score
- 908460
- 0.212208495548303
- 908465
- 0.206960413533671
- 910399
- 0.199472418370317
- 687816
- 0.210476703599609
- 724873
- 0.208599987840974
- 722045
- 0.205912328570108
- 724868
- 0.206824422476044
- 724866
- 0.206680734703705
- 687798
- 0.216285826784939
- 908475
- 0.211272650817056
- 908463
- 0.212709167064642
- 753608
- 0.214862714858326
- 910931
- 0.208238535189379
- 1240202
- 0.221460628591852
- 908472
- 0.199364266689227
- 910900
- 0.206122945311186
- 687799
- 0.207275870549291
- 722058
- 0.211096427406128
- 753600
- 0.196932229169657
- 1298348
- 0.220622285821822
- 1240185
- 0.205585230366729
- 908476
- 0.208683186256861
- 1298350
- 0.214824656364828
- 907786
- 0.213957161925956
- 753592
- 0.208851848262052
- 906805
- 0.206267698997767
- 724859
- 0.199211692897873
- 905949
- 0.205975609580769
- 1240146
- 0.201502450359524
- 909721
- 0.209140034748241
- 724878
- 0.200425672922387
- 905972
- 0.197447093167736
- 905970
- 0.214730379524127
- 1240145
- 0.208943355941955
- 1290908
- 0.212126278350503
- 1503370
- 0.205649917002817
- 1247873
- 0.212802015899291
- 906791
- 0.213492024817614
- 1503369
- 0.215298916088862
- 687820
- 0.207231800649215
- 908473
- 0.213490138123903
- 687800
- 0.223231513028422
- 687821
- 0.206601909617251
- 905967
- 0.209417767289827
- 687807
- 0.203234420099537
- 1240187
- 0.202968045468593
- 722066
- 0.19802808950732
- 724874
- 0.213749514385265
- 1240190
- 0.205352198153527
- 687802
- 0.210866998533979
- 687812
- 0.206712523847301
- 687819
- 0.212651066975101
- 905942
- 0.206288622478281
- 905944
- 0.197146764420502
- 924244
- 0.205988603788793
- 713869
- 0.180809653345578
- 1298347
- 0.218419961891229
- 1298537
- 0.217770564747933
- 687777
- 0.205889757940065
- 724834
- 0.209015402840839
In [36]:
drug_res <- luad_Osimertinib %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
head(drug_res)
| COSMIC_ID | LN_IC50 | AUC | |
|---|---|---|---|
| <dbl> | <dbl> | <dbl> | |
| 1 | 687777 | 0.105057 | 0.827993 |
| 2 | 687798 | 0.196595 | 0.868151 |
| 3 | 687799 | -0.701592 | 0.800871 |
| 4 | 687800 | 0.407872 | 0.844857 |
| 5 | 687802 | 1.111300 | 0.861017 |
| 6 | 687807 | 3.462617 | 0.960185 |
In [40]:
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [47]:
#cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method = 'pearson')
#cor.test(drug_res$LN_IC50,drug_res$score,method = 'kendall')
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method = 'spearman')
Spearman's rank correlation rho
data: drug_res$LN_IC50 and drug_res$score
S = 45740, p-value = 0.01831
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2709086
Spearman's rank correlation rho
data: drug_res$AUC and drug_res$score
S = 45068, p-value = 0.02606
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2522367
In [43]:
?cor.test
In [53]:
normalize <- function(v) {
(v - min(v)) / (max(v) - min(v))
}
FUN2 <- function(x){
sum(x*normalize(score_df$weight_shap_total_mean))
#sum(x)
}
FUN3 <- function(x){
mean(x*normalize(score_df$weight_shap_total_mean))
#sum(x)
}
score <- apply(score_df[,2:61],MARGIN = 2,FUN = FUN2)
In [54]:
drug_res$score2 = score[as.character(drug_res$COSMIC_ID)]
In [55]:
cor.test(drug_res$LN_IC50,drug_res$score2,alternative = 'less',method = 'spearman')
cor.test(drug_res$AUC,drug_res$score2,alternative = 'less',method = 'spearman')
Spearman's rank correlation rho
data: drug_res$LN_IC50 and drug_res$score2
S = 45936, p-value = 0.01645
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2763545
Spearman's rank correlation rho
data: drug_res$AUC and drug_res$score2
S = 44496, p-value = 0.03461
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2363434
In [60]:
FUN3 <- function(x){
mean(x*normalize(score_df$weight_shap_total_mean))
#sum(x)
}
score <- apply(score_df[,2:61],MARGIN = 2,FUN = FUN3)
drug_res$score3 = score[as.character(drug_res$COSMIC_ID)]
In [63]:
cor.test(drug_res$LN_IC50,drug_res$score3,alternative = 'less',method = 'spearman')
cor.test(drug_res$AUC,drug_res$score3,alternative = 'less',method = 'spearman')
Spearman's rank correlation rho
data: drug_res$LN_IC50 and drug_res$score3
S = 45936, p-value = 0.01645
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2763545
Spearman's rank correlation rho
data: drug_res$AUC and drug_res$score3
S = 44496, p-value = 0.03461
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2363434
In [62]:
drug_res
| COSMIC_ID | LN_IC50 | AUC | score | score2 | score3 |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| 687777 | 0.105057 | 0.827993 | 0.2058898 | 82.70539 | 3.308216 |
| 687798 | 0.196595 | 0.868151 | 0.2162858 | 86.67149 | 3.466860 |
| 687799 | -0.701592 | 0.800871 | 0.2072759 | 83.41327 | 3.336531 |
| 687800 | 0.407872 | 0.844857 | 0.2232315 | 89.30361 | 3.572144 |
| 687802 | 1.111300 | 0.861017 | 0.2108670 | 84.53376 | 3.381350 |
| 687807 | 3.462617 | 0.960185 | 0.2032344 | 82.19164 | 3.287666 |
| 687812 | 1.773792 | 0.939289 | 0.2067125 | 82.56441 | 3.302576 |
| 687816 | 3.072226 | 0.954529 | 0.2104767 | 85.08433 | 3.403373 |
| 687819 | 2.648910 | 0.966616 | 0.2126511 | 85.02986 | 3.401194 |
| 687820 | 3.553966 | 0.946425 | 0.2072318 | 82.89718 | 3.315887 |
| 687821 | 3.333193 | 0.930423 | 0.2066019 | 82.63207 | 3.305283 |
| 713869 | 2.788954 | 0.968042 | 0.1808097 | 72.84235 | 2.913694 |
| 722045 | 0.675624 | 0.881735 | 0.2059123 | 82.43843 | 3.297537 |
| 722058 | 1.873261 | 0.980054 | 0.2110964 | 85.16990 | 3.406796 |
| 722066 | 2.874454 | 0.985684 | 0.1980281 | 80.19137 | 3.207655 |
| 724834 | 3.411423 | 0.970948 | 0.2090154 | 84.29881 | 3.371952 |
| 724859 | 3.481590 | 0.968356 | 0.1992117 | 79.71792 | 3.188717 |
| 724866 | 3.228194 | 0.956801 | 0.2066807 | 82.36860 | 3.294744 |
| 724868 | 2.193570 | 0.978127 | 0.2068244 | 83.17306 | 3.326923 |
| 724873 | 2.373773 | 0.961620 | 0.2086000 | 84.05954 | 3.362382 |
| 724874 | 1.810679 | 0.898188 | 0.2137495 | 86.51462 | 3.460585 |
| 724878 | 4.609979 | 0.977902 | 0.2004257 | 80.81991 | 3.232796 |
| 753592 | 2.343446 | 0.975225 | 0.2088518 | 83.85678 | 3.354271 |
| 753600 | 0.882932 | 0.917099 | 0.1969322 | 78.22661 | 3.129064 |
| 753608 | -3.304365 | 0.518041 | 0.2148627 | 86.82620 | 3.473048 |
| 905942 | 2.942427 | 0.988078 | 0.2062886 | 83.20768 | 3.328307 |
| 905944 | 1.852038 | 0.960971 | 0.1971468 | 79.09347 | 3.163739 |
| 905949 | 1.212945 | 0.919610 | 0.2059756 | 81.96329 | 3.278532 |
| 905967 | -1.380772 | 0.723152 | 0.2094178 | 83.77889 | 3.351156 |
| 905970 | 1.164936 | 0.875131 | 0.2147304 | 85.66564 | 3.426626 |
| 905972 | 2.742171 | 0.955952 | 0.1974471 | 78.95932 | 3.158373 |
| 906791 | 2.841505 | 0.969501 | 0.2134920 | 85.94674 | 3.437869 |
| 906805 | -1.382719 | 0.707402 | 0.2062677 | 82.40678 | 3.296271 |
| 907786 | 1.639399 | 0.964106 | 0.2139572 | 86.37881 | 3.455152 |
| 908460 | 5.459908 | 0.965804 | 0.2122085 | 85.83336 | 3.433334 |
| 908463 | 3.702049 | 0.954930 | 0.2127092 | 85.78408 | 3.431363 |
| 908465 | 0.645906 | 0.860003 | 0.2069604 | 83.00452 | 3.320181 |
| 908472 | 3.168900 | 0.982703 | 0.1993643 | 79.80822 | 3.192329 |
| 908473 | 0.447561 | 0.834463 | 0.2134901 | 85.19267 | 3.407707 |
| 908475 | 1.890945 | 0.940001 | 0.2112727 | 85.28980 | 3.411592 |
| 908476 | 2.091601 | 0.952126 | 0.2086832 | 83.13667 | 3.325467 |
| 909721 | 4.560097 | 0.969773 | 0.2091400 | 83.67314 | 3.346926 |
| 910399 | 1.636913 | 0.954137 | 0.1994724 | 79.41703 | 3.176681 |
| 910900 | 1.780943 | 0.961797 | 0.2061229 | 82.62925 | 3.305170 |
| 910931 | 1.983289 | 0.968638 | 0.2082385 | 83.49648 | 3.339859 |
| 924244 | -3.272124 | 0.521202 | 0.2059886 | 82.99434 | 3.319773 |
| 1240145 | 2.473396 | 0.970796 | 0.2089434 | 84.39852 | 3.375941 |
| 1240146 | -3.590724 | 0.485425 | 0.2015025 | 81.32593 | 3.253037 |
| 1240185 | 1.088707 | 0.900065 | 0.2055852 | 82.65903 | 3.306361 |
| 1240187 | 3.062089 | 0.965789 | 0.2029680 | 81.16429 | 3.246572 |
| 1240190 | 2.151088 | 0.949050 | 0.2053522 | 82.63214 | 3.305285 |
| 1240202 | -2.604536 | 0.591882 | 0.2214606 | 89.11581 | 3.564632 |
| 1247873 | -2.523354 | 0.594091 | 0.2128020 | 86.15723 | 3.446289 |
| 1290908 | 0.963438 | 0.901244 | 0.2121263 | 85.79743 | 3.431897 |
| 1298347 | 2.215420 | 0.988440 | 0.2184200 | 87.37037 | 3.494815 |
| 1298348 | 0.469193 | 0.855017 | 0.2206223 | 88.59530 | 3.543812 |
| 1298350 | -0.348410 | 0.824295 | 0.2148247 | 86.47263 | 3.458905 |
| 1298537 | 1.415262 | 0.946008 | 0.2177706 | 87.65842 | 3.506337 |
| 1503369 | -0.042516 | 0.856887 | 0.2152989 | 86.48633 | 3.459453 |
| 1503370 | 0.664314 | 0.886320 | 0.2056499 | 81.94553 | 3.277821 |
In [65]:
drivers$directions = drivers$weight_grad_total_dir_mean >= 0
In [73]:
drivers_pos = drivers %>% filter(drivers$directions)
drivers_neg = drivers %>% filter(!drivers$directions)
In [75]:
score_df <- merge(luad_Osimertinib_exp_m,drivers_pos,by = 'V1',all = F)
score <- apply(score_df[,2:61],MARGIN = 2,FUN = FUN1)
drug_res <- luad_Osimertinib %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [81]:
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method = 'spearman')
Spearman's rank correlation rho
data: drug_res$LN_IC50 and drug_res$score
S = 50644, p-value = 0.0006783
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.4071687
Spearman's rank correlation rho
data: drug_res$AUC and drug_res$score
S = 49744, p-value = 0.001378
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.3821617
In [82]:
score_df <- merge(luad_Osimertinib_exp_m,drivers_neg,by = 'V1',all = F)
score <- apply(score_df[,2:61],MARGIN = 2,FUN = FUN1)
drug_res <- luad_Osimertinib %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [87]:
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method = 'spearman')
Spearman's rank correlation rho
data: drug_res$LN_IC50 and drug_res$score
S = 37518, p-value = 0.3734
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.04245624
Spearman's rank correlation rho
data: drug_res$AUC and drug_res$score
S = 37770, p-value = 0.3534
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.04945818
In [89]:
FUN4 <- function(x){
mean(x*score_df$weight_shap_total_mean)
#sum(x)
}
score_df <- merge(luad_Osimertinib_exp_m,drivers_pos,by = 'V1',all = F)
score <- apply(score_df[,2:61],MARGIN = 2,FUN = FUN4)
drug_res <- luad_Osimertinib %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [90]:
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method = 'spearman')
Spearman's rank correlation rho
data: drug_res$LN_IC50 and drug_res$score
S = 50644, p-value = 0.0006783
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.4071687
Spearman's rank correlation rho
data: drug_res$AUC and drug_res$score
S = 49744, p-value = 0.001378
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.3821617
In [93]:
drivers$directions_cal = as.numeric(drivers$directions)
drivers[!drivers$directions,]$directions_cal = -1
In [94]:
drivers
| V1 | weight_shap_total_mean | weight_shap_total_std | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | directions | directions_cal |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> |
| CD24 | 0.0008392735 | 3.266406e-04 | -5.656427e-05 | 10 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| GTF2B | 0.0010324697 | 3.080479e-04 | -1.316672e-04 | 10 | TRUE | FALSE | FALSE | FALSE | FALSE | -1 |
| MSH6 | 0.0016612993 | 4.270973e-04 | 7.425508e-05 | 10 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| TFDP1 | 0.0013715578 | 5.694479e-04 | -5.785423e-05 | 10 | TRUE | FALSE | FALSE | FALSE | FALSE | -1 |
| MYO1B | 0.0010195644 | 2.886838e-04 | 1.748662e-04 | 10 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| TUBA1B | 0.0018392079 | 3.183509e-04 | -1.412048e-04 | 10 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| SLC1A5 | 0.0021976048 | 6.430204e-04 | 1.082030e-04 | 10 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| TGM2 | 0.0007415661 | 2.308803e-04 | 5.989809e-06 | 10 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| BARD1 | 0.0021673360 | 6.107930e-04 | 1.084314e-04 | 10 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| TUBB4B | 0.0006375848 | 2.468592e-04 | 1.148352e-05 | 10 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| RRBP1 | 0.0006809303 | 2.972012e-04 | -1.221697e-05 | 9 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| ANXA2 | 0.0013427509 | 6.148865e-04 | -7.150776e-05 | 9 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| PLK2 | 0.0011322402 | 5.536364e-04 | 6.720297e-05 | 9 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| PSMC4 | 0.0009214443 | 3.828258e-04 | -4.451585e-05 | 9 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| DGKE | 0.0009730622 | 2.876285e-04 | -5.831443e-05 | 9 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| CEBPD | 0.0006121597 | 2.758779e-04 | 7.729752e-05 | 9 | TRUE | FALSE | FALSE | FALSE | TRUE | 1 |
| HSPA2 | 0.0006995864 | 2.921626e-04 | -2.506919e-04 | 8 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| RAB31 | 0.0011450001 | 3.613637e-04 | -5.959134e-05 | 8 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| TFAP2C | 0.0002400025 | 8.345076e-05 | 1.130995e-05 | 7 | TRUE | FALSE | FALSE | FALSE | TRUE | 1 |
| CD9 | 0.0020897718 | 4.177533e-04 | -1.463574e-04 | 6 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| TPM1 | 0.0003337573 | 1.718587e-04 | 2.991494e-05 | 6 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| LMO7 | 0.0006043960 | 2.033280e-04 | 2.352714e-04 | 6 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| KLF5 | 0.0003624802 | 2.617805e-04 | 1.071805e-04 | 6 | TRUE | FALSE | FALSE | FALSE | TRUE | 1 |
| TRIB1 | 0.0004753006 | 2.120645e-04 | 2.118026e-05 | 6 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
| HIST1H1C | 0.0003803697 | 1.031536e-04 | -7.409843e-05 | 4 | FALSE | FALSE | FALSE | FALSE | FALSE | -1 |
| DAAM1 | 0.0020700805 | 4.885557e-04 | 1.041791e-04 | 4 | FALSE | FALSE | FALSE | FALSE | TRUE | 1 |
In [96]:
FUN5 <- function(x){
sum(x*score_df$weight_shap_total_mean*score_df$directions_cal)
#sum(x)
}
score_df <- merge(luad_Osimertinib_exp_m,drivers,by = 'V1',all = F)
score <- apply(score_df[,2:61],MARGIN = 2,FUN = FUN5)
drug_res <- luad_Osimertinib %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>% filter(COSMIC_ID %in% names(score))
drug_res$score = score[as.character(drug_res$COSMIC_ID)]
In [97]:
cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$AUC,drug_res$score,alternative = 'less',method = 'spearman')
Spearman's rank correlation rho
data: drug_res$LN_IC50 and drug_res$score
S = 47080, p-value = 0.008474
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.3081412
Spearman's rank correlation rho
data: drug_res$AUC and drug_res$score
S = 46064, p-value = 0.01532
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2799111
load CCLE¶
In [246]:
CCLE_exp <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_DepMap_18Q1_RNAseq_RPKM_20180214.gct')
In [247]:
CCLE_exp
| Name | Description | 22RV1_PROSTATE | 2313287_STOMACH | 253JBV_URINARY_TRACT | 253J_URINARY_TRACT | 42MGBA_CENTRAL_NERVOUS_SYSTEM | 5637_URINARY_TRACT | 59M_OVARY | 639V_URINARY_TRACT | ⋯ | PEDS015T_SOFT_TISSUE | RT11284_URINARY_TRACT | SMSCTR_SOFT_TISSUE | SMZ1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | SW982_SOFT_TISSUE | SYO1_SOFT_TISSUE | TC138_BONE | TC205_BONE | UPCISCC152_UPPER_AERODIGESTIVE_TRACT | UW228_CENTRAL_NERVOUS_SYSTEM |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| ENSG00000223972.4 | DDX11L1 | 0.00000 | 0.03755 | 0.00000 | 0.06507 | 0.00000 | 0.01378 | 0.01463 | 0.03085 | ⋯ | 0.00000 | 0.05286 | 0.02024 | 0.02800 | 0.00000 | 0.03535 | 0.00000 | 0.00000 | 0.00000 | 0.03799 |
| ENSG00000227232.4 | WASH7P | 12.63011 | 10.14155 | 6.20657 | 6.55835 | 7.61752 | 6.19356 | 4.62277 | 6.00767 | ⋯ | 6.13757 | 6.73907 | 11.10122 | 8.39780 | 5.06211 | 7.12056 | 10.75343 | 6.56657 | 15.23348 | 6.47732 |
| ENSG00000243485.2 | MIR1302-11 | 0.04289 | 0.01037 | 0.16955 | 0.23364 | 0.08765 | 0.00000 | 0.00808 | 0.09373 | ⋯ | 0.05747 | 0.16061 | 0.07825 | 0.07425 | 0.14717 | 0.03906 | 0.24068 | 0.29759 | 0.07448 | 0.01049 |
| ENSG00000237613.2 | FAM138A | 0.00000 | 0.00869 | 0.22423 | 0.16558 | 0.00000 | 0.00637 | 0.00000 | 0.01427 | ⋯ | 0.00000 | 0.00000 | 0.07491 | 0.01555 | 0.01298 | 0.00000 | 0.04480 | 0.01039 | 0.02674 | 0.00879 |
| ENSG00000268020.2 | OR4G4P | 0.00000 | 0.00000 | 0.02886 | 0.03875 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.01205 | 0.00000 | 0.00000 | 0.00000 | 0.01442 | 0.05347 | 0.00000 | 0.00000 |
| ENSG00000240361.1 | OR4G11P | 0.00000 | 0.00000 | 0.01939 | 0.03904 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00336 | 0.00000 | 0.00000 | 0.01452 | 0.01347 | 0.00000 | 0.02279 |
| ENSG00000186092.4 | OR4F5 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00344 | 0.00000 | 0.00000 | 0.01487 | 0.02758 | 0.00000 | 0.00000 |
| ENSG00000238009.2 | RP11-34P13.7 | 0.44714 | 0.34327 | 0.16982 | 0.08922 | 0.09428 | 0.10706 | 0.03342 | 0.04230 | ⋯ | 0.02377 | 0.16913 | 0.11099 | 0.40697 | 0.04486 | 0.04848 | 0.09957 | 0.07182 | 0.04402 | 0.06945 |
| ENSG00000233750.3 | CICP27 | 0.37909 | 0.09168 | 0.10995 | 0.09146 | 0.07513 | 0.06523 | 0.06276 | 0.13009 | ⋯ | 0.02309 | 0.21900 | 0.17666 | 0.12014 | 0.03734 | 0.08369 | 0.08595 | 0.10628 | 0.08265 | 0.01686 |
| ENSG00000237683.5 | AL627309.1 | 6.17780 | 6.56648 | 1.03405 | 0.76888 | 1.01231 | 4.03005 | 3.29219 | 5.23439 | ⋯ | 0.62840 | 5.91599 | 0.90505 | 1.95612 | 0.47254 | 1.12027 | 0.29244 | 0.25216 | 1.91472 | 0.21741 |
| ENSG00000268903.1 | RP11-34P13.15 | 5.52172 | 5.79291 | 0.96543 | 0.91141 | 0.93642 | 3.20103 | 3.01556 | 2.80014 | ⋯ | 0.93254 | 1.30318 | 0.77101 | 0.70282 | 0.30376 | 0.30372 | 0.01808 | 0.26829 | 1.29501 | 0.02838 |
| ENSG00000269981.1 | RP11-34P13.16 | 4.10155 | 5.14582 | 0.28874 | 0.29075 | 0.28361 | 2.95516 | 2.26559 | 4.87078 | ⋯ | 0.20659 | 6.24651 | 1.24589 | 2.10197 | 0.94677 | 1.50955 | 0.48071 | 0.13373 | 2.18038 | 0.49040 |
| ENSG00000239906.1 | RP11-34P13.14 | 2.16921 | 3.73763 | 1.24117 | 1.22142 | 1.80095 | 2.04499 | 2.22189 | 3.98638 | ⋯ | 0.09082 | 4.43075 | 0.14135 | 1.04632 | 0.04897 | 0.46300 | 0.04227 | 0.11759 | 0.73994 | 0.00000 |
| ENSG00000241860.2 | RP11-34P13.13 | 1.45051 | 1.70260 | 0.51623 | 0.47688 | 0.30626 | 0.28475 | 0.36352 | 0.94654 | ⋯ | 0.02052 | 0.84705 | 0.11792 | 0.13970 | 0.04979 | 0.21083 | 0.12121 | 0.27384 | 0.20167 | 0.07782 |
| ENSG00000222623.1 | RNU6-1100P | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000241599.1 | RP11-34P13.9 | 0.03833 | 0.04635 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.09786 | 0.00000 | 0.00691 | 0.00000 | 0.02182 | 0.00000 | 0.02770 | 0.00000 | 0.00000 |
| ENSG00000228463.4 | AP006222.2 | 1.39546 | 0.81420 | 1.49090 | 1.67532 | 1.12489 | 0.78417 | 0.31503 | 0.70630 | ⋯ | 0.32892 | 0.64095 | 0.12701 | 1.67112 | 0.23401 | 0.28241 | 1.20156 | 1.14947 | 0.18683 | 0.66111 |
| ENSG00000241670.2 | AP006222.1 | 3.42004 | 1.26904 | 4.06619 | 4.54950 | 3.18034 | 1.54135 | 1.29531 | 2.44463 | ⋯ | 1.01020 | 1.04723 | 0.30657 | 4.33317 | 0.78430 | 0.96816 | 1.83345 | 1.51713 | 0.17956 | 2.14708 |
| ENSG00000237094.7 | RP4-669L17.10 | 5.40033 | 5.15221 | 1.80699 | 1.60018 | 2.11839 | 0.97418 | 2.47912 | 1.61543 | ⋯ | 0.77455 | 0.81067 | 1.59256 | 0.55472 | 0.73843 | 0.42826 | 0.40135 | 0.45318 | 0.68839 | 0.17951 |
| ENSG00000250575.1 | RP4-669L17.8 | 5.38639 | 5.68387 | 1.14718 | 0.94784 | 1.24236 | 1.29203 | 2.75635 | 1.35521 | ⋯ | 1.11283 | 1.45587 | 3.63882 | 0.88714 | 1.54464 | 0.66789 | 0.56195 | 0.72548 | 1.85007 | 0.46692 |
| ENSG00000233653.3 | CICP7 | 0.55159 | 0.20949 | 0.15817 | 0.10742 | 0.11200 | 0.06275 | 0.09325 | 0.27045 | ⋯ | 0.06316 | 0.03611 | 0.05069 | 0.01913 | 0.00319 | 0.01208 | 0.05512 | 0.01533 | 0.00877 | 0.00000 |
| ENSG00000224813.2 | RP4-669L17.4 | 2.57880 | 0.11767 | 0.25309 | 0.15291 | 0.19887 | 0.43172 | 0.32080 | 0.29000 | ⋯ | 0.05433 | 0.08282 | 0.00000 | 0.03509 | 0.04394 | 0.00000 | 0.07585 | 0.14067 | 0.00000 | 0.00000 |
| ENSG00000235249.1 | OR4F29 | 0.05281 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00781 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.01498 | 0.00000 | 0.01270 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000269732.1 | WBP1LP7 | 0.19302 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.02747 | 0.00000 | 0.00000 | 0.02993 | 0.02951 |
| ENSG00000256186.1 | AL732372.1 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000236743.1 | RP5-857K21.15 | 0.20729 | 0.00000 | 0.00000 | 0.02714 | 0.01324 | 0.31038 | 0.00000 | 0.02574 | ⋯ | 0.01447 | 0.02205 | 0.01688 | 0.14017 | 0.02340 | 0.00000 | 0.04039 | 0.03746 | 0.00000 | 0.00000 |
| ENSG00000236601.1 | RP4-669L17.2 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000236679.2 | RP4-669L17.1 | 0.88719 | 0.24756 | 0.35499 | 0.47661 | 0.32543 | 0.18166 | 0.29997 | 0.22597 | ⋯ | 0.22859 | 0.11616 | 0.02965 | 0.04102 | 0.06162 | 0.05179 | 0.07092 | 0.29595 | 0.05643 | 0.05565 |
| ENSG00000231709.1 | RP5-857K21.1 | 0.52008 | 0.07546 | 0.06493 | 0.03269 | 0.12754 | 0.47992 | 0.03919 | 0.11366 | ⋯ | 0.01161 | 0.03541 | 0.04067 | 0.21007 | 0.00939 | 0.05920 | 0.45399 | 0.04511 | 0.02580 | 0.00000 |
| ENSG00000235146.2 | RP5-857K21.2 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.11301 | 0.03127 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| ENSGR0000270726.1 | AJ271736.10 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSGR0000185203.7 | WASIR1 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSGR0000227159.3 | DDX11L16 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000210049.1 | MT-TF | 0.00000 | 0.00000 | 0.00000 | 0.25845 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 1.04983 | 0.16076 | 0.00000 | 0.00000 | 0.28085 | 0.38457 | 0.17831 | 0.00000 | 0.45267 |
| ENSG00000211459.2 | MT-RNR1 | 910.27307 | 469.22092 | 1786.56653 | 2176.14771 | 884.79218 | 301.08878 | 2614.87988 | 675.11743 | ⋯ | 1555.49182 | 1269.64868 | 377.95169 | 395.04962 | 452.11700 | 973.65546 | 981.28265 | 1460.00427 | 555.72089 | 291.52655 |
| ENSG00000210077.1 | MT-TV | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.14172 | 0.21605 | 0.00000 | 0.04578 | 0.00000 | 0.28899 | 0.59357 | 0.36696 | 0.00000 | 0.00000 |
| ENSG00000210082.2 | MT-RNR2 | 5638.01953 | 1781.06616 | 7849.11230 | 16147.12598 | 2471.76538 | 1613.67700 | 8680.26855 | 1838.13623 | ⋯ | 5183.72119 | 3302.51294 | 1707.74731 | 1253.85999 | 855.83307 | 10336.67676 | 3690.90479 | 7264.39111 | 2183.91016 | 1826.41577 |
| ENSG00000209082.1 | MT-TL1 | 1.98519 | 0.14120 | 0.24297 | 0.00000 | 0.11932 | 0.82890 | 0.65993 | 0.23200 | ⋯ | 0.78230 | 0.19877 | 0.60874 | 0.29479 | 0.10544 | 0.26587 | 0.54609 | 1.85682 | 0.00000 | 0.42853 |
| ENSG00000198888.2 | MT-ND1 | 2925.43628 | 1367.65308 | 1888.47595 | 2255.65625 | 984.25763 | 1918.51343 | 2427.98145 | 2112.54736 | ⋯ | 2769.03271 | 2440.45386 | 1518.37463 | 901.30054 | 381.61768 | 1091.19629 | 1445.38892 | 3173.04883 | 1121.96899 | 618.01868 |
| ENSG00000210112.1 | MT-TM | 0.00000 | 0.00000 | 0.13399 | 0.00000 | 0.00000 | 0.00000 | 0.36393 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.16785 | 0.13935 | 0.34890 | 0.00000 | 0.40153 | 0.74471 | 0.00000 | 0.15755 |
| ENSG00000198763.3 | MT-ND2 | 4228.48145 | 2871.87549 | 2761.64624 | 5103.22412 | 2417.21167 | 2361.06470 | 2872.39697 | 3078.08496 | ⋯ | 2306.98755 | 3569.08911 | 1356.70166 | 1019.21259 | 432.39215 | 960.28937 | 1100.50708 | 2589.74121 | 836.28326 | 544.17621 |
| ENSG00000210117.1 | MT-TW | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.04645 | 0.00000 | 0.00000 | 0.20077 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000210127.1 | MT-TA | 0.12693 | 0.00000 | 0.26410 | 0.26594 | 0.00000 | 0.00000 | 0.00000 | 0.25217 | ⋯ | 1.84237 | 0.21605 | 0.66168 | 0.54931 | 0.22923 | 1.58942 | 1.18715 | 2.20176 | 0.94467 | 0.15526 |
| ENSG00000210135.1 | MT-TN | 0.35993 | 0.43520 | 1.49775 | 1.75956 | 0.49037 | 0.31936 | 1.35601 | 0.59589 | ⋯ | 2.81305 | 4.08428 | 0.78178 | 2.81238 | 1.30000 | 3.14124 | 2.80524 | 7.63077 | 2.97635 | 1.02730 |
| ENSG00000198804.2 | MT-CO1 | 15661.66016 | 15261.75098 | 11303.64844 | 19603.06836 | 2913.64453 | 6795.08105 | 11937.98340 | 7864.29932 | ⋯ | 8555.61133 | 9931.27441 | 3680.04346 | 4611.66553 | 2439.67969 | 5117.47998 | 7931.24316 | 13101.27344 | 4357.16064 | 3762.57886 |
| ENSG00000210151.2 | MT-TS1 | 68.79620 | 87.32864 | 28.91843 | 65.55353 | 9.07903 | 25.45277 | 36.34377 | 22.06525 | ⋯ | 59.09744 | 59.19841 | 19.02321 | 26.87028 | 16.61893 | 27.88717 | 38.78011 | 69.17211 | 28.02511 | 22.66865 |
| ENSG00000210154.1 | MT-TD | 0.12880 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000198712.1 | MT-CO2 | 11730.29785 | 12858.86426 | 11931.20117 | 35615.90234 | 8594.03711 | 7163.49072 | 11873.46191 | 9486.36133 | ⋯ | 7809.95996 | 16700.29102 | 6524.20996 | 6030.77588 | 3623.32739 | 6427.56055 | 8503.33203 | 18922.68164 | 4888.17285 | 4039.84033 |
| ENSG00000210156.1 | MT-TK | 0.00000 | 0.00000 | 0.13016 | 0.00000 | 0.38354 | 0.00000 | 0.11784 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.54258 | 0.00000 | 0.00000 |
| ENSG00000210164.1 | MT-TG | 0.25759 | 0.15573 | 0.40197 | 0.00000 | 0.13161 | 0.00000 | 0.00000 | 0.25588 | ⋯ | 0.43141 | 0.21923 | 0.33570 | 0.00000 | 0.34890 | 0.00000 | 0.20077 | 0.18618 | 0.15976 | 0.15755 |
| ENSG00000198840.2 | MT-ND3 | 1176.81189 | 1009.34979 | 2816.78296 | 7116.30469 | 1049.68384 | 1070.77808 | 1605.18481 | 1925.49426 | ⋯ | 2968.00879 | 5217.97168 | 1408.86121 | 870.05261 | 730.42224 | 1027.34473 | 1461.33569 | 2883.65967 | 995.56390 | 441.25647 |
| ENSG00000210174.1 | MT-TR | 0.00000 | 0.00000 | 0.00000 | 0.14115 | 0.13768 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.30088 | 0.22935 | 0.52680 | 0.09719 | 0.00000 | 0.15338 | 0.00000 | 0.00000 | 0.16713 | 0.16482 |
| ENSG00000210176.1 | MT-TH | 0.00000 | 0.00000 | 0.39614 | 0.26594 | 0.00000 | 0.00000 | 0.23910 | 0.00000 | ⋯ | 0.42516 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.39572 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000210184.1 | MT-TS2 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.13171 | 0.00000 | 0.29492 | ⋯ | 1.16019 | 0.00000 | 0.19346 | 0.05353 | 0.00000 | 0.33797 | 0.23139 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000210191.1 | MT-TL2 | 0.12335 | 0.00000 | 0.51331 | 1.03378 | 0.25209 | 0.43780 | 0.34855 | 0.24507 | ⋯ | 0.00000 | 0.83987 | 0.00000 | 0.13346 | 0.11138 | 0.14042 | 0.76914 | 0.35662 | 0.30602 | 0.00000 |
| ENSG00000198786.2 | MT-ND5 | 2393.80298 | 1960.96802 | 1896.28870 | 3522.37695 | 1178.93579 | 1345.42151 | 1943.89148 | 1413.57471 | ⋯ | 1770.68628 | 2219.26221 | 684.31268 | 952.33801 | 512.84479 | 972.97974 | 1865.05200 | 2017.67957 | 793.58710 | 757.41357 |
| ENSG00000198695.2 | MT-ND6 | 1825.52332 | 2056.64697 | 2184.88647 | 4568.00195 | 1616.96484 | 1189.86279 | 1906.90405 | 1469.75574 | ⋯ | 1483.81250 | 1337.68286 | 352.41855 | 397.95502 | 310.11151 | 606.44336 | 1043.02661 | 1204.54675 | 593.61139 | 472.01627 |
| ENSG00000210194.1 | MT-TE | 0.25386 | 0.00000 | 0.26410 | 0.00000 | 0.12970 | 0.00000 | 0.00000 | 0.25217 | ⋯ | 0.14172 | 0.43211 | 0.33084 | 0.18310 | 0.22923 | 0.43348 | 0.39572 | 0.18348 | 0.31489 | 0.15526 |
| ENSG00000198727.2 | MT-CYB | 4923.78223 | 3827.00537 | 1905.18481 | 4590.01416 | 2343.92407 | 1902.01685 | 2026.15710 | 1603.98230 | ⋯ | 3631.88745 | 4311.98926 | 1825.74780 | 1135.89490 | 668.37408 | 1153.96252 | 2453.64722 | 3853.68872 | 1645.22168 | 973.56439 |
| ENSG00000210195.2 | MT-TT | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.14357 | 0.00000 | 0.00000 | 0.41370 | 0.19182 | 0.00000 | 0.16232 |
In [248]:
CCLE_info <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/sample_info.csv')
In [249]:
CCLE_info
| cell_line | n_replicates | clean_cell_line_name | cell_line_SSMD | SSMD_failure | culture_type | culture_medium | culture_code | aliases | primary_tissue | histology | histology_subtype | type | tumor_type | Cas9_activity |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <int> | <chr> | <dbl> | <lgl> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> |
| 143B_BONE | 1 | 143B | -2.0856446 | FALSE | Adherent | EMEM; 10% FBS; 0.015 mg/ml 5-bromo-2'-seoxyuridine | 143B | bone | bone | NS | osteosarcoma | Osteosarcoma | 11 | |
| 253J_URINARY_TRACT | 1 | 253J | -1.8428910 | FALSE | DMEM: 90.0% | 253J | urinary_tract | urinary_tract | carcinoma | urinary_tract | Bladder | 11 | ||
| 42MGBA_CENTRAL_NERVOUS_SYSTEM | 2 | 42MGBA | -1.3369782 | FALSE | RPMI 1640 + EMEM (1:1): 80.0% | 42MGBA | central_nervous_system | glioma | glioma | astrocytoma | Glioma | 27.4 | ||
| 5637_URINARY_TRACT | 2 | 5637 | -1.9308829 | FALSE | light Adherent or suspansion | RPMI-1640: 90.0% | 5637 | 4.9 | ||||||
| 59M_OVARY | 2 | 59M | -1.2884236 | FALSE | Adherent | DMEM; 10% FBS + 2 mM Glutamine, sodium pyruvate, ITS | 59M | ovary | ovary | carcinoma | ovary | Ovarian | 5.7 | |
| 639V_URINARY_TRACT | 1 | 639V | -1.6057682 | FALSE | Adherent | DMEM; 10% FBS | 639V | urinary_tract | urinary_tract | carcinoma | urinary_tract | Bladder | 20.3 | |
| 647V_URINARY_TRACT | 2 | 647V | -1.5523345 | FALSE | DMEM; 15% FBS, 2mMGlutamax-1 | 647V | urinary_tract | urinary_tract | carcinoma | urinary_tract | Bladder | 14.2 | ||
| 769P_KIDNEY | 2 | 769P | -1.6189531 | FALSE | RPMI;10% FBS; | 769P | kidney | kidney | carcinoma | renal_clear_cell | Kidney | 16.7 | ||
| 786O_KIDNEY | 3 | 786O | -1.3468784 | FALSE | RPMI; 10% FBS | 786-O | kidney | kidney | carcinoma | renal_clear_cell | Kidney | 36.5 | ||
| 8305C_THYROID | 2 | 8305C | -0.9969040 | FALSE | RPMI-1640: 85.0% | 8305C | thyroid | thyroid | carcinoma | thyroid | Thyroid | 8.6 | ||
| 8MGBA_CENTRAL_NERVOUS_SYSTEM | 2 | 8MGBA | -0.8486761 | FALSE | EMEM: 80.0% | 8MGBA | central_nervous_system | glioma | glioma | astrocytoma | Glioma | 45.7 | ||
| A2058_SKIN | 2 | A2058 | -1.3985050 | FALSE | Adherent | DMEM; 10% FBS | A2058 | melanoma | skin | malignant_melanoma | melanoma | Skin | 0 | |
| A2780_OVARY | 2 | A2780 | -2.4762733 | FALSE | RPMI; 10% FBS | A2780 | ovary | ovary | carcinoma | ovarian_adenocarcinoma;unlikely | Ovarian | 16.7 | ||
| A549_LUNG | 4 | A549 | -1.9398550 | FALSE | Adherent | DMEM; 10% FBS | DMEM001 | A549 | lung_NSC | lung | non_small_cell_carcinoma | lung_non_small | Lung | 17.2 |
| ABC1_LUNG | 3 | ABC1 | -1.9669010 | FALSE | EMEM; 10% FBS | ABC-1 | lung_NSC | lung | carcinoma | lung_non_small | Lung | 22.5 | ||
| AGS_STOMACH | 2 | AGS | -1.6651098 | FALSE | F12K; 10% FBS | AGS | stomach | stomach | adenocarcinoma | gastric_adenocarcinoma | Gastric | 16.3 | ||
| ASPC1_PANCREAS | 4 | ASPC1 | -0.8058231 | FALSE | RPMI; 10% FBS | RPMI001 | AsPC-1 | pancreas | pancreatic_exocrine | pancreatic_ductal_adenocarcinoma | pancreas_carcinoma | Pancreas | 57.2 | |
| AU565_BREAST | 2 | AU565 | -1.4317646 | FALSE | adherent | DMEM; 10% FBS | AU565 | breast | breast | Luminal | breast_carcinoma | Breast | 36.2 | |
| BC3C_URINARY_TRACT | 2 | BC3C | -1.3567518 | FALSE | Adherent | M10 | BC3C | urinary_tract | urinary_tract | carcinoma | urinary_tract | Bladder | 4.9 | |
| BFTC905_URINARY_TRACT | 2 | BFTC905 | -1.3863104 | FALSE | DMEM: 90.0% | BFTC905 | urinary_tract | urinary_tract | carcinoma | urinary_tract | Bladder | 38.8 | ||
| BFTC909_KIDNEY | 2 | BFTC909 | -1.0006993 | FALSE | DMEM;10 % FBS; | BFTC909 | kidney | kidney | carcinoma | renal_carcinoma | Kidney | 38.8 | ||
| BHY_UPPER_AERODIGESTIVE_TRACT | 2 | BHY | -1.2361349 | FALSE | Adherent | DMEM;10% FBS; | BHY | upper_aerodigestive | upper_aerodigestive_tract | carcinoma | head_and_neck_squamous | Head and Neck | 8.8 | |
| BICR22_UPPER_AERODIGESTIVE_TRACT | 2 | BICR22 | -1.5166298 | FALSE | Adherent | DMEM; 10% FBS; 2mM Glutamine; 0.4ug/ml hydrocortisone | BICR 22 | upper_aerodigestive | upper_aerodigestive_tract | carcinoma | head_and_neck_squamous | Head and Neck | 5.6 | |
| BICR56_UPPER_AERODIGESTIVE_TRACT | 2 | BICR56 | -1.9335539 | FALSE | DMEM; 10% FBS; 2mM Glutamine; 0.4ug/ml hydrocortisone | BICR 56 | upper_aerodigestive | upper_aerodigestive_tract | carcinoma | head_and_neck_squamous | Head and Neck | 19.3 | ||
| BICR6_UPPER_AERODIGESTIVE_TRACT | 2 | BICR6 | -1.1369064 | FALSE | Adherent | DMEM;10% FBS; | BICR6 | upper_aerodigestive | upper_aerodigestive_tract | carcinoma | head_and_neck_squamous | Head and Neck | 10.6 | |
| BIN67_OVARY | 2 | BIN67 | -1.4820323 | FALSE | 20% FBS, 40% DMEM, 40% DMEM/F12 | BIN-67 | ovary | ovarian_rhabdoid | SCCOHT | rhabdoid_tumor | Rhabdoid | 57 | ||
| BT549_BREAST | 3 | BT549 | -1.3533033 | FALSE | Adherent | RPMI-1640: 10% heat inactivated FBS | BT-549 | breast | breast | Basal | breast_carcinoma | Breast | 22.6 | |
| C2BBE1_LARGE_INTESTINE | 3 | C2BBE1 | -1.1272783 | FALSE | D10+ITS+Glu | C2BBe1 | large_intestine | colorectal | adenocarcinoma | colon_carcinoma | Colon | 5.8 | ||
| C32_SKIN | 2 | C32 | -1.0697073 | FALSE | EMEM; 10% FBS; 0.1mM NEAA | C32 | melanoma | skin | malignant_melanoma | melanoma | Skin | 33.9 | ||
| CAKI1_KIDNEY | 2 | CAKI1 | -1.3984047 | FALSE | McCoy's 5A; 10% FBS | Caki-1 | kidney | kidney | carcinoma | renal_clear_cell | Kidney | 49.5 | ||
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| TF1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | 3 | TF1 | -1.3036890 | FALSE | suspension | RPMI-1640: 10%FBS; 2ng/ml GM-CSF | TF1 | AML | haematopoietic_and_lymphoid_tissue | haematopoietic_neoplasm | AML | AML | good | |
| THP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | 3 | THP1 | -0.9557363 | FALSE | suspension | RPMI-1640: 10%FBS; BME (0.05mM) | THP-1 | AML | haematopoietic_and_lymphoid_tissue | haematopoietic_neoplasm | AML | AML | 34.4 | |
| TOV21G_OVARY | 3 | TOV21G | -2.1112380 | FALSE | MCDB 105:Medium 199 (1:1); 15% FBS | TOV-21G | ovary | ovary | carcinoma | ovarian_clear_cell; hypermutated | Ovarian | 37.7 | ||
| TUHR10TKB_KIDNEY | 2 | TUHR10TKB | -1.1968951 | FALSE | Adherent | RPMI-1640: 90.0% | TUHR10TKB | kidney | kidney | carcinoma | renal_carcinoma | Kidney | 54.8 | |
| TUHR4TKB_KIDNEY | 1 | TUHR4TKB | -1.0834388 | FALSE | Adherent | DMEM;10% FBS; | TUHR4TKB | kidney | kidney | carcinoma | renal_carcinoma | Kidney | 22.8 | |
| U118MG_CENTRAL_NERVOUS_SYSTEM | 2 | U118MG | -1.0066358 | FALSE | DMEM; 10% FBS | U-118 MG | central_nervous_system | glioma | glioma | astrocytoma | Glioma | 30.9 | ||
| U178_CENTRAL_NERVOUS_SYSTEM | 2 | U178 | -0.7408038 | FALSE | DMEM; 10% FBS | U178 | glioma | central_nervous_system | glioma | glioblastoma | Glioblastoma | 51.7 | ||
| U251MG_CENTRAL_NERVOUS_SYSTEM | 2 | U251MG | -2.2653716 | FALSE | DMEM; 10% FBS | U251-MG | central_nervous_system | glioma | glioma | astrocytoma | Glioma | 11.3 | ||
| U2OS_BONE | 2 | U2OS | -1.5726655 | FALSE | Adherent | McCoy's 5A; 10% FBS | U2-OS | bone | bone | osteosarcoma | osteosarcoma | Osteosarcoma | 52.6 | |
| U343_CENTRAL_NERVOUS_SYSTEM | 2 | U343 | -1.5622078 | FALSE | DMEM; 10% FBS | U343 | glioma | central_nervous_system | glioma | glioblastoma | Glioblastoma | 17.8 | ||
| U87MG_CENTRAL_NERVOUS_SYSTEM | 2 | U87MG | -0.7999304 | FALSE | EMEM; 10% FBS | U-87 MG | central_nervous_system | glioma | glioma | astrocytoma | Glioma | 37.9 | ||
| U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | 3 | U937 | -1.2477302 | FALSE | suspension | RPMI;10% FBS; | U-937 | lymphoma_DLBCL | haematopoietic_and_lymphoid_tissue | lymphoid_neoplasm | AML | AML | 23.9 | |
| UACC257_SKIN | 1 | UACC257 | -1.2689274 | FALSE | Adherent | RPMI-1640; 10%FBS | RPMI001 | UACC257 | melanoma | skin | malignant_melanoma | melanoma | Skin | 18.7 |
| UACC62_SKIN | 2 | UACC62 | -1.1422030 | FALSE | RPMI-1640: 10%FBS | UACC-62 | melanoma | skin | malignant_melanoma | melanoma | Skin | 26.9 | ||
| UMCHOR1_BONE | 3 | UM-CHOR1 | -1.2465131 | FALSE | Um-Chor1 | bone | chordoma | sarcoma | sarcoma | Bone | 10.3 | |||
| UMUC1_URINARY_TRACT | 2 | UMUC1 | -1.4589930 | FALSE | EMEM; 10% FBS, 2mMGlutamax-1, 1xNEAA | UMUC1 | urinary_tract | urinary_tract | carcinoma | urinary_tract | Bladder | 33.7 | ||
| UMUC3_URINARY_TRACT | 2 | UMUC3 | -1.4348565 | FALSE | EMEM; 10% FBS | UMUC3 | urinary_tract | urinary_tract | carcinoma | urinary_tract | Bladder | 11.7 | ||
| UOK101_KIDNEY | 2 | UOK101 | -1.3615701 | FALSE | Adherent | DMEM; 10% FBS | UOK101 | kidney | kidney | carcinoma | renal_clear_cell | Kidney | 38.7 | |
| UPCISCC152_UPPER_AERODIGESTIVE_TRACT | 2 | UPCISCC152 | -1.0926038 | FALSE | EMEM; 10% FBS, 2mMGlutamax-1 | UPCI:SCC152 | upper_aerodigestive | upper_aerodigestive_tract | carcinoma | head_and_neck_squamous | Head and Neck | NO VALUES 8.16.16 | ||
| UW228_CENTRAL_NERVOUS_SYSTEM | 1 | UW228 | -1.5177330 | FALSE | D:F10+Glu | UW228 | central_nervous_system | central_nervous_system | medulloblastoma | medulloblastoma | Medulloblastoma | 12.3 | ||
| VMCUB1_URINARY_TRACT | 2 | VMCUB1 | -1.5920114 | FALSE | DMEM: 90.0%; 10%FBS | VMCUB1 | urinary_tract | urinary_tract | carcinoma | urinary_tract | Bladder | 32.8 | ||
| WM115_SKIN | 2 | WM115 | -0.9006826 | FALSE | Adherent | EMEM: 10% FBS | WM-115 | melanoma | skin | malignant_melanoma | melanoma | Skin | 51.7 | |
| WM1799_SKIN | 2 | WM1799 | -0.9749160 | FALSE | 0 | RPMI-1640: 10%FBS | WM1799 | melanoma | skin | malignant_melanoma | melanoma | Skin | 30.8 | |
| WM2664_SKIN | 2 | WM2664 | -1.5681949 | FALSE | DMEM; 10% FBS | WM-266-4 | melanoma | skin | malignant_melanoma | melanoma | Skin | 28.5 | ||
| WM793_SKIN | 2 | WM793 | -1.5361338 | FALSE | RPMI-1640: 10%FBS | WM-793 | melanoma | skin | malignant_melanoma | melanoma | Skin | 45 | ||
| WM983B_SKIN | 2 | WM983B | -1.2323335 | FALSE | RPMI-1640: 10%FBS | WM-983B | melanoma | skin | malignant_melanoma | melanoma | Skin | 13.1 | ||
| YAPC_PANCREAS | 4 | YAPC | -1.4144762 | FALSE | RPMI-1640: 10%FBS | pancreas | pancreatic_exocrine | adenocarcinoma | pancreas_carcinoma | Pancreas | 7 | |||
| YD38_UPPER_AERODIGESTIVE_TRACT | 2 | YD38 | -1.3743563 | FALSE | Adherent | RPMI-1640; 10%FBS | YD-38 | upper_aerodigestive | upper_aerodigestive_tract | carcinoma | head_and_neck_squamous | Head and Neck | 16.1 | |
| YKG1_CENTRAL_NERVOUS_SYSTEM | 2 | YKG1 | -1.3185945 | FALSE | Adherent | DMEM; 10% FBS | YKG1 | central_nervous_system | glioblastoma | Glioblastoma | 21.1 | |||
| ZR751_BREAST | 2 | ZR751 | -1.5258912 | FALSE | Adherent | RPMI; 10% FBS | ZR-75-1 | breast | breast | Luminal | breast_carcinoma | Breast | 20.9 |
In [250]:
table(CCLE_info$tumor_type)
AML Bladder Bone
1 10 20 3
Breast Cervical Colon Endometrial
26 1 25 14
Esophageal Ewing's Gastric Glioblastoma
10 4 11 20
glioma Glioma Head and Neck Kidney
1 9 12 17
Liver Lung Lymphoma Medulloblastoma
13 48 8 7
Mesothelioma Myeloma Neuroblastoma Osteosarcoma
1 7 15 5
Other Ovarian Pancreas Rhabdoid
9 31 22 3
RMS Sarcoma Skin Synovial Sarcoma
6 1 29 2
T-ALL Thyroid
2 1
In [251]:
CCLE_drug <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_NP24.2009_Drug_data_2015.02.24.csv')
In [252]:
CCLE_drug
| CCLE Cell Line Name | Primary Cell Line Name | Compound | Target | Doses (uM) | Activity Data (median) | Activity SD | Num Data | FitType | EC50 (uM) | IC50 (uM) | Amax | ActArea |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <int> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> |
| 1321N1_CENTRAL_NERVOUS_SYSTEM | 1321N1 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 8.67,11.0,2.16,.27,-10,-13,-26,-43 | 3.31,3.72,5.36,4.67,13.1,.18,2.42,7.51 | 8 | Sigmoid | 8.7177736 | 8.0000000 | -42.558014 | 0.7124 |
| 22RV1_PROSTATE | 22Rv1 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | .94,12.5,-14,4.16,-25,-32,-52,-71 | 1.95,13.3,6.98,21.8,16.0,18.8,4.84,7.93 | 8 | Sigmoid | 8.1651636 | 2.3299241 | -71.589340 | 1.6723 |
| 42MGBA_CENTRAL_NERVOUS_SYSTEM | 42-MG-BA | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 8.91,8.39,-3.5,12.4,-.55,-6.2,-48,-63 | 13.7,7.70,11.1,6.43,24.0,9.57,9.57,10.4 | 8 | Sigmoid | 1.5145085 | 2.6821299 | -63.491371 | 1.1852 |
| 5637_URINARY_TRACT | 5637 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 2.15,9.91,-3.5,.056,-2.1,-14,-30,-62 | 4.05,9.75,12.7,4.36,11.0,10.0,24.6,.14 | 8 | Sigmoid | 8.0065952 | 5.0023141 | -62.352776 | 0.9948 |
| 639V_URINARY_TRACT | 639-V | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 11.8,-7.3,-9.4,-15,-11,-21,-53,-50 | .95,5.67,11.1,.68,31.6,22.3,1.45,3.08 | 8 | Sigmoid | 0.9311957 | 1.7361814 | -51.959808 | 1.5436 |
| 697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | 697 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 18.4,4.65,8.64,19.3,-19,-20,-21,-78 | 25.4,1.92,15.0,23.1,24.7,13.5,1.26,2.51 | 8 | Sigmoid | 8.7006545 | 4.2608218 | -73.333786 | 1.7665 |
| 769P_KIDNEY | 769-P | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 2.58,-4.1,6.18,-7.0,-13,-40,-28,-47 | 18.1,12.4,12.4,5.80,5.24,10.3,6.56,6.84 | 8 | Sigmoid | 0.3062428 | 8.0000000 | -39.688095 | 1.4376 |
| 786O_KIDNEY | 786-O | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.2,-.53,.55,-4.1,2.41,-11,-8.8,-52 | 14.5,15.3,7.11,9.90,4.66,3.60,2.58,11.5 | 8 | Sigmoid | 5.2676673 | 7.6131477 | -51.686317 | 0.5954 |
| 8305C_THYROID | 8305C | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 1.00,-1.9,8.83,9.38,-18,-17,-28,-68 | 6.49,3.67,4.67,6.24,6.70,3.11,9.45,7.52 | 8 | Sigmoid | 8.5207715 | 4.9506359 | -66.228836 | 1.2929 |
| 8505C_THYROID | 8505C | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -6.0,-.21,-2.4,6.80,-6.3,1.35,-19,-22 | .63,13.0,4.25,6.36,3.61,7.19,6.61,2.70 | 8 | Sigmoid | 1.8466899 | 8.0000000 | -22.268826 | 0.3189 |
| 8MGBA_CENTRAL_NERVOUS_SYSTEM | 8-MG-BA | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -6.0,-23,-17,9.18,-2.6,-20,-34,-91 | 7.42,9.33,2.72,5.46,28.5,1.39,3.31,1.07 | 8 | Sigmoid | 8.2129965 | 3.8883409 | -91.339981 | 1.5025 |
| A172_CENTRAL_NERVOUS_SYSTEM | A172 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -.27,.084,-2.7,-6.1,-6.3,-6.1,-15,-22 | 5.84,1.59,2.77,5.89,2.43,3.25,4.54,7.73 | 8 | Sigmoid | 8.4196215 | 8.0000000 | -21.366783 | 0.3615 |
| A204_SOFT_TISSUE | A-204 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 3.12,2.35,2.69,-5.1,6.80,-24,-6.7,-68 | 5.56,3.35,2.22,2.93,2.93,2.31,1.72,12.5 | 8 | Sigmoid | 4.1637120 | 5.1503119 | -67.193253 | 0.5525 |
| A2058_SKIN | A2058 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.97,-2.9,1.30,1.23,-1.3,-9.4,-12,-93 | 3.91,.85,4.79,1.27,4.97,7.31,6.77,2.91 | 8 | Sigmoid | 8.7312970 | 5.5130911 | -92.368950 | 1.0006 |
| A253_SALIVARY_GLAND | A-253 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 2.93,3.40,.080,-4.7,-1.6,-24,-12,-22 | 4.57,16.8,1.79,.29,1.18,1.76,4.17,7.84 | 8 | Constant | NA | 8.0000000 | -4.688773 | 0.6375 |
| A2780_OVARY | A2780 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 6.95,1.20,-.26,1.52,-3.7,-14,-19,-47 | 10.9,9.43,4.29,8.48,2.84,9.36,3.68,17.7 | 8 | Sigmoid | 8.7356707 | 6.5089040 | -57.180573 | 0.6674 |
| A375_SKIN | A-375 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 1.92,4.74,4.50,-6.6,-7.7,-27,-47,-80 | 2.97,1.72,.44,4.55,2.36,2.60,1.62,1.18 | 8 | Sigmoid | 8.0439944 | 2.7554235 | -79.608070 | 1.4484 |
| A549_LUNG | A549 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 5.54,5.62,-.69,-7.7,-.50,-24,-60,-102 | 2.46,.16,6.07,.64,3.96,5.91,2.04,.94 | 8 | Sigmoid | 3.2991312 | 1.9547869 | -101.633217 | 1.6406 |
| A673_BONE | A-673 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -3.6,5.42,-4.6,19.0,-6.1,-42,-55,-70 | 9.41,4.84,6.69,13.4,5.04,5.36,5.28,4.39 | 8 | Sigmoid | 0.6276461 | 1.1276772 | -65.029343 | 1.5990 |
| ACHN_KIDNEY | ACHN | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 3.24,9.14,-4.2,4.02,-1.5,-10,-11,-53 | 1.52,5.92,2.67,.47,2.59,3.12,1.67,5.78 | 8 | Sigmoid | 8.7098522 | 7.6729093 | -52.142548 | 0.6207 |
| ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | ALL-SIL | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.8,2.14,1.10,12.0,-17,-10,-50,-78 | 3.85,5.52,5.32,15.4,6.82,1.01,7.05,3.44 | 8 | Sigmoid | 2.1170161 | 2.5584965 | -78.507507 | 1.8822 |
| AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | AMO-1 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 5.90,13.2,-14,-7.1,-13,-45,-80,-99 | 21.3,9.53,3.20,13.0,34.1,23.9,6.67,.26 | 8 | Linear | NA | 0.1945099 | -98.842896 | 2.8999 |
| AN3CA_ENDOMETRIUM | AN3 CA | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 19.0,15.0,4.05,-1.4,1.59,-8.2,-12,-14 | 3.66,6.75,13.3,26.7,14.1,9.39,6.78,7.74 | 8 | Sigmoid | 0.2750051 | 8.0000000 | -15.036425 | 0.3505 |
| ASPC1_PANCREAS | AsPC-1 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.82,2.54,-4.9,-.16,-13,-30,-67,-80 | 12.1,16.4,10.8,17.2,.96,17.0,3.41,5.90 | 8 | Sigmoid | 1.1814655 | 1.4277748 | -77.212585 | 1.3966 |
| AU565_BREAST | AU565 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 15.3,5.67,7.83,3.45,-12,-5.9,-20,-43 | 20.4,4.88,21.4,12.2,16.7,3.44,9.70,1.59 | 8 | Sigmoid | 9.0651752 | 8.0000000 | -40.801186 | 0.7651 |
| AZ521_STOMACH | AZ-521 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 9.23,-2.4,8.19,-30,-18,-52,-58,-89 | 3.53,4.18,4.04,3.50,26.5,12.6,7.65,7.72 | 8 | Sigmoid | 8.5428888 | 1.1525109 | -87.210060 | 2.3462 |
| BCPAP_THYROID | B-CPAP | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.2,1.18,.057,-2.1,-18,-18,-24,-70 | 4.67,5.92,2.38,7.66,.58,1.62,.76,41.8 | 8 | Sigmoid | 8.8890763 | 5.1052547 | -67.504158 | 1.3166 |
| BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | BDCM | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | -16,4.32,-20,-3.4,-5.7,21.7,-13,-51 | 24.2,18.6,2.31,11.1,.48,4.36,27.4,9.40 | 8 | Linear | NA | 4.9726346 | -52.246094 | 0.3316 |
| BFTC909_KIDNEY | BFTC-909 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | 2.87,-3.2,-1.9,-11,-12,-17,-18,-47 | 2.83,6.38,4.83,3.66,10.0,6.03,6.80,1.60 | 8 | Sigmoid | 8.8399991 | 8.0000000 | -43.887810 | 0.8980 |
| BGC823_STOMACH | BGC-823 | AEW541 | IGF1R | .0025,.0080,.025,.080,.25,.80,2.53,8 | .027,1.62,8.48,-2.4,-10,-35,-48,-81 | 9.29,1.79,6.72,9.61,8.35,4.59,1.26,1.61 | 8 | Sigmoid | 6.2132106 | 2.3974571 | -79.739655 | 1.5611 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| TE15_OESOPHAGUS | TE-15 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 27.5,21.7,16.2,11.6,-8.1,-47,-72,-79 | 8.28,29.9,34.4,16.6,17.1,5.79,2.43,1.53 | 8 | Sigmoid | 0.4123355 | 0.8777253 | -79.5809860 | 1.67700 |
| TE5_OESOPHAGUS | TE-5 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.60,8.55,8.76,9.92,1.44,5.70,-12,-15 | 5.08,3.61,1.28,3.55,7.63,6.59,4.44,8.50 | 8 | Sigmoid | 1.5344867 | 8.0000000 | -14.7724943 | 0.23000 |
| TE617T_SOFT_TISSUE | TE 617.T | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 5.00,21.3,-1.5,11.9,.86,-23,-70,-88 | 7.93,21.7,3.87,22.6,26.0,4.65,2.36,1.15 | 8 | Sigmoid | 1.3595777 | 1.5173497 | -88.6974411 | 1.72010 |
| TE9_OESOPHAGUS | TE-9 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | .18,13.1,-6.1,6.61,-7.4,2.33,-35,-45 | 1.99,16.8,8.36,12.4,2.42,20.8,.31,12.8 | 8 | Sigmoid | 1.9807538 | 8.0000000 | -45.5231514 | 0.92590 |
| TEN_ENDOMETRIUM | TEN | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -.25,14.3,4.08,9.07,-4.0,4.08,-13,14.2 | 13.4,15.7,15.5,29.8,8.45,.32,15.1,4.28 | 8 | Constant | NA | 8.0000000 | 3.8391488 | 0.08303 |
| TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | Toledo | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -2.4,14.1,-5.6,.54,3.10,-11,10.0,-3.8 | 2.19,24.3,15.3,4.00,44.3,20.2,10.1,32.4 | 8 | Constant | NA | 8.0000000 | 0.6750307 | 0.01286 |
| TOV112D_OVARY | TOV-112D | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.5,1.69,-12,-17,-2.1,-13,-13,-47 | 9.04,7.23,15.7,7.13,3.82,9.99,16.5,3.52 | 8 | Sigmoid | 8.3923747 | 8.0000000 | -46.4707794 | 0.87770 |
| TOV21G_OVARY | TOV-21G | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 11.0,1.04,16.7,2.26,-4.4,-1.4,-19,-57 | 2.41,2.46,6.73,2.72,2.55,3.55,2.99,10.2 | 8 | Sigmoid | 8.5395064 | 6.9190626 | -56.1040001 | 0.88570 |
| TT2609C02_THYROID | TT2609-C02 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 30.1,18.2,1.56,28.8,-5.0,5.53,-31,-47 | 36.4,1.63,14.5,16.0,11.2,21.1,21.8,13.8 | 8 | Sigmoid | 2.4789515 | 8.0000000 | -48.0713501 | 0.82220 |
| TYKNU_OVARY | TYK-nu | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 20.3,-1.9,15.1,-6.4,13.9,-.18,28.6,-14 | 12.9,1.56,12.7,11.0,7.84,12.3,17.4,5.13 | 8 | Constant | NA | 8.0000000 | 7.2842770 | 0.20760 |
| U118MG_CENTRAL_NERVOUS_SYSTEM | U-118 MG | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -3.4,-7.6,-.63,-.024,10.3,-8.3,.32,-6.5 | 11.3,15.4,1.85,1.75,.18,10.7,11.2,12.5 | 8 | Constant | NA | 8.0000000 | -0.2980179 | 0.05681 |
| U2OS_BONE | U-2 OS | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 2.79,5.16,2.32,2.76,-8.9,-10,-5.4,-28 | 1.18,15.3,5.45,6.35,5.14,.31,6.00,17.9 | 8 | Sigmoid | 8.8328905 | 8.0000000 | -25.1368809 | 0.48880 |
| U87MG_CENTRAL_NERVOUS_SYSTEM | U-87 MG | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 7.54,4.25,16.3,-2.3,10.2,16.7,8.94,15.4 | 12.2,5.84,7.91,21.8,6.70,2.95,16.1,9.94 | 8 | Constant | NA | 8.0000000 | 11.6490555 | 0.07387 |
| U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | U-937 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 10.6,3.38,16.2,18.5,5.05,-9.5,-15,-21 | 4.88,6.03,4.61,12.1,10.7,9.62,1.64,16.6 | 8 | Sigmoid | 2.0305276 | 8.0000000 | -31.2440090 | 0.54950 |
| UACC257_SKIN | UACC-257 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 5.15,5.38,5.97,8.88,4.36,12.4,-3.6,-2.1 | 2.34,7.66,.52,6.22,.72,4.02,1.72,2.50 | 8 | Constant | NA | 8.0000000 | 4.7888308 | 0.00000 |
| UACC62_SKIN | UACC-62 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 10.8,1.57,-.13,-3.2,-7.8,-16,-24,-51 | 10.3,6.66,7.20,3.85,9.92,12.1,4.80,6.57 | 8 | Sigmoid | 8.6142205 | 8.0000000 | -48.5191650 | 0.68730 |
| UACC812_BREAST | UACC-812 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 10.7,14.4,-15,12.7,9.02,14.9,-13,-43 | 17.1,35.3,24.8,26.3,30.8,25.5,51.0,42.1 | 8 | Linear | NA | 0.9121245 | -71.8577805 | 0.44330 |
| UMUC3_URINARY_TRACT | UM-UC-3 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -.80,7.97,5.99,4.26,8.03,3.29,4.63,2.22 | 14.3,1.50,3.14,.31,3.59,2.20,2.17,.72 | 8 | Constant | NA | 8.0000000 | 4.6135664 | 0.00000 |
| VMRCLCD_LUNG | VMRC-LCD | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 33.9,21.8,12.5,23.1,51.2,6.46,19.9,-12 | 8.91,4.00,9.78,9.23,28.5,8.53,27.8,.88 | 8 | Constant | NA | 8.0000000 | 19.1242733 | 0.25470 |
| VMRCRCW_KIDNEY | VMRC-RCW | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -8.3,-6.0,11.8,-.80,21.7,2.57,.021,-26 | 15.8,1.05,1.64,4.76,6.68,11.6,17.1,16.8 | 8 | Sigmoid | 4.2276740 | 8.0000000 | -26.2453327 | 0.19400 |
| VMRCRCZ_KIDNEY | VMRC-RCZ | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 1.09,8.22,11.8,-5.1,11.3,1.30,-15,-24 | 3.08,15.8,25.2,8.47,11.0,9.54,.40,8.20 | 8 | Sigmoid | 2.1785133 | 8.0000000 | -23.6163578 | 0.90170 |
| WM115_SKIN | WM-115 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 13.5,2.34,10.1,5.14,11.9,-2.7,2.68,-26 | 10.3,20.1,2.86,5.23,3.62,21.2,23.6,6.39 | 8 | Sigmoid | 2.2557328 | 8.0000000 | -26.2989388 | 0.10350 |
| WM1799_SKIN | WM1799 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 6.35,8.58,28.7,11.9,1.05,-8.2,-8.8,-42 | 6.96,10.3,15.1,5.25,4.21,15.5,11.9,.88 | 8 | Sigmoid | 8.4941912 | 8.0000000 | -40.7798424 | 0.68190 |
| WM2664_SKIN | WM-266-4 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.6,9.92,-4.9,6.90,-2.8,8.43,-12,-1.3 | 7.77,3.55,1.71,4.37,10.9,5.51,12.5,13.1 | 8 | Constant | NA | 8.0000000 | 3.8058126 | 0.08816 |
| WM793_SKIN | WM-793 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -.050,3.17,.15,2.59,-4.0,10.3,-12,-17 | 14.0,16.4,4.83,11.5,1.51,17.5,6.24,17.9 | 8 | Constant | NA | 8.0000000 | -4.0406976 | 0.07185 |
| WM88_SKIN | WM-88 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 1.99,-1.1,6.73,13.1,-4.1,-4.3,2.53,-14 | 3.25,3.41,7.44,11.5,.45,6.40,1.28,5.15 | 8 | Constant | NA | 8.0000000 | 0.5926009 | 0.24200 |
| WM983B_SKIN | WM-983B | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 17.2,15.1,4.57,26.9,-3.3,-2.0,-2.8,-3.1 | 24.1,24.5,18.1,21.7,5.15,8.95,9.30,48.2 | 8 | Constant | NA | 8.0000000 | 2.2413831 | 0.98100 |
| YKG1_CENTRAL_NERVOUS_SYSTEM | YKG1 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 1.31,-4.4,-2.9,-1.3,-.39,-5.0,-9.3,-16 | 1.72,3.30,6.16,2.33,4.65,4.73,3.47,2.82 | 8 | Constant | NA | 8.0000000 | -3.1719000 | 0.07841 |
| ZR751_BREAST | ZR-75-1 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 39.3,-25,31.8,25.5,10.6,-37,-22,-37 | 38.8,7.84,7.22,3.57,.86,23.7,18.6,8.01 | 8 | Linear | NA | 8.0000000 | -34.8354607 | 1.17170 |
| ZR7530_BREAST | ZR-75-30 | Erlotinib | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -23,9.04,8.59,26.0,17.9,12.1,-11,-22 | 15.7,1.31,1.58,22.9,26.8,23.5,32.8,19.9 | 8 | Linear | NA | 8.0000000 | -22.0538826 | 0.03998 |
In [253]:
any(CCLE_drug$Compound == 'Osimertinib')
FALSE
In [254]:
table(CCLE_drug$Compound)
17-AAG AEW541 AZD0530 AZD6244 Erlotinib Irinotecan
503 503 504 503 503 317
L-685458 Lapatinib LBW242 Nilotinib Nutlin-3 Paclitaxel
491 504 503 420 504 503
Panobinostat PD-0325901 PD-0332991 PF2341066 PHA-665752 PLX4720
500 504 434 504 503 496
RAF265 Sorafenib TAE684 TKI258 Topotecan ZD-6474
460 503 504 504 504 496
In [255]:
table(CCLE_drug$Target)
ABL ALK c-MET CDK4 EGFR FGFR GS HDAC HSP90 IGF1R MDM2 MEK RAF 924 504 1007 434 1503 504 491 500 503 503 504 1007 956 RTK TOP1 TUBB1 XIAP 503 821 503 503
In [256]:
CCLE_drug %>% filter(CCLE_drug$Target == 'EGFR') %>% dplyr::select(Compound) %>% table()
Compound
Erlotinib Lapatinib ZD-6474
503 504 496
In [257]:
CCLE_drug_info <- readxl::read_excel('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_GNF_data_090613.xls')
In [258]:
CCLE_drug_info
| GNF_REG_ID | compound name | other name | DESCRIPTION | 5637 | 22Rv1 | 786-O | A-204 | A-253 | A2780 | ⋯ | THP-1 | Toledo | TOV-112D | U-87MG | U-937 | UACC-257 | VMRC-RCW | WM-266-4 | YH13 | ZR-75-1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | ⋯ | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> | <chr> |
| GNF-00-0325-0295-7 | PHA-665752 | NA | [Pha-665752,5-((2,6-dichlorobenzyl)sulfonyl)-3-((3,5-dimethyl-4-((2-(pyrrolidin-1-ylmethyl)pyrrolidin-1-yl)carbonyl)-1H-pyrrol-2-yl)methylene)-1,3-dihydro-2H-indol-2-one][CAS:477575-56-7][MOA:Apoptosis-inducer; Cytostatic; Tyrosine-kinase-inhibi ...] | >20 | >20 | 9.0899999999999999 | >20 | >20 | >20 | ⋯ | >20 | 13.24 | >20 | >20 | >20 | 16.489999999999998 | 18.809999999999999 | 18.170000000000002 | >20 | >20 |
| GNF-00-0519-9793-4 | AZD6244 | Selumetinib | [Selumetinib,AZD 6244][MOA:Apoptosis-inducer; Cytostatic; Mek-1-protein-kinase-inhibitor][Target:Mek1][GNF Document:ALK\GNF March 2009 MEKEFF.ppt, B-Raf\2008-10-14-MEKFF-ODB-final.ppt][Patent:N3 alkylated benzimidazole derivatives as mek inhibit ...] | >20 | >20 | >20 | >20 | 0.16059999999999999 | >20 | ⋯ | >20 | >20 | >20 | >20 | >20 | 0.01257 | >20 | 0.018239999999999999 | >20 | >20 |
| GNF-00-0520-3101-3 | Tozasertib | VX680 | [Tozasertib,Vx680][CAS:639089-54-6][MOA:Abl-tyrosine-kinase-inhibitor; Apoptosis-inducer; Cytostatic; Synergist; Tyrosine-kinase-inhibitor][Indication:Aurora kinase inhibitor highest phase:Phase 2 - Acute lymphoblastic leukemia, Chronic myelocyt ...] | 0.2094 | 0.051499999999999997 | 0.64100000000000001 | 0.025059999999999999 | 0.065000000000000002 | 0.067500000000000004 | ⋯ | 0.02307 | 0.061800000000000001 | 0.029090000000000001 | 0.1113 | 0.024039999999999999 | 0.71099999999999997 | 6.1500000000000004 | 0.048899999999999999 | 0.046800000000000001 | 0.033500000000000002 |
| GNF-00-0526-5527-3 | Nutlin-3 | NA | [Analog][nutlin-3a,nutlin-3][CAS:548472-68-0][MOA:Apoptosis-inducer; Cell-cycle-inhibitor; Cytostatic; Mdm2-p53-binding-protein-inhibitor; Ubiquitin-ligase-inhibitor][Target:P53 kinase, MDM2/p53, Mdm2, MDM2 oncogene, MDM2 Oncogene][GNF Document: ...] | >20 | 9.7300000000000004 | >20 | 3.5600000000000001 | >20 | 14.24 | ⋯ | >20 | >20 | 15.83 | >20 | >20 | 1.1060000000000001 | >20 | 1.252 | >20 | 2.2650000000000001 |
| GNF-00-0526-5536-4 | Sorafenib | Nexavar | [Sorafenib(in Wikipedia),Nexavar][CAS:284461-73-0,475207-59-1][MOA:Antineoplastic Agents; Protein Kinase Inhibitors][Indication:For the treatment of patients with advanced renal cell carcinoma.][Toxicity:Most of the dose-limiting toxicities incl ...] | >20 | >20 | >20 | 1.631 | >20 | >20 | ⋯ | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 |
| GNF-00-0526-5570-6 | PF-2341066 | Crizotinib | [pf-2341066][MOA:Apoptosis-inducer; Cyclic-amp-agonist; Cytostatic][Indication:C-MET tyrosine kinase antagonist highest phase:Phase 1 - Cancer, Stomach tumor][Target:Hgfr, Hepatocyte growth factor receptor, C-MET][GNF Document:ROS\Ros07-02-08MX. ...] | 9.1400000000000006 | 2.915 | 8.1099999999999994 | 1.248 | 4.4299999999999997 | 4.1100000000000003 | ⋯ | 0.97299999999999998 | 1.464 | 0.39600000000000002 | >20 | 0.79200000000000004 | 6.8700000000000001 | 14.18 | 3.54 | 7.6600000000000001 | 7.0199999999999996 |
| GNF-00-0526-9948-6 | Staurosporine | NA | [Staurosporine(in Wikipedia),Sch-47112][CAS:62996-74-1,622996-74-1][MOA:Enzyme Inhibitors][Indication:Protein kinase C inhibitor; CDK1 inhibitor; CDK4 inhibitor highest phase:Research Tool - Cancer, Hypertension; Platelet biosynthesis stimulant; ...] | 0.0076400000000000001 | 0.0096399999999999993 | 0.0045599999999999998 | <0.001221 | 0.037199999999999997 | 0.0935 | ⋯ | 0.0064200000000000004 | <0.001221 | 0.0070200000000000002 | 0.0099299999999999996 | 0.0055900000000000004 | 0.033300000000000003 | 0.0060699999999999999 | 0.0064799999999999996 | <0.001221 | 0.038399999999999997 |
| GNF-00-0527-1619-5 | Docetaxel | Taxotere | [Docetaxel(in Wikipedia),Taxotere][CAS:114977-28-5,148408-66-6][MOA:Antineoplastic Agents][Indication:For the treatment of patients with locally advanced or metastatic breast cancer after failure of prior chemotherapy. Also used as a single agen ...] | <0.001221 | <0.001221 | <0.001221 | <0.001221 | <0.001221 | <0.001221 | ⋯ | <0.001221 | <0.001221 | <0.001221 | <0.001221 | <0.001221 | >20 | 0.018519999999999998 | <0.001221 | <0.001221 | 0.00415 |
| GNF-00-0527-1633-3 | Paclitaxel | Taxol | [Paclitaxel(in Wikipedia),Paclitaxel - MGI GP][CAS:33069-62-4,197778-55-5][MOA:Antineoplastic Agents Phytogenic; Tubulin Modulators][Warning:Discontinued-II][Indication:Paclitaxel is a potent inhibitor of eukaryotic cell replication, blocking ce ...] | <0.001221 | <0.001221 | 0.01102 | <0.001221 | <0.001221 | <0.001221 | ⋯ | NoFit | <0.001221 | <0.001221 | NoFit | <0.001221 | NoFit | 0.069699999999999998 | <0.001221 | <0.001221 | <0.001221 |
| GNF-00-0527-1635-5 | L-685458 | NA | [L-682679,L-685458][CAS:126409-24-3,132565-31-2][MOA:Virucide; Gamma-secretase-inhibitor][Target:HIV Protease, Gamma secretase, Beta-amyloid protein, Secretase, Gamma secretase, Gamma Secretase][Crystal:1IZH|Inhibitor Of Hiv Protease With Unusua ...] | >20 | >20 | >20 | >20 | >20 | >20 | ⋯ | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 |
| GNF-00-0527-1636-6 | Irinotecan | Camptosar | [Irinotecan(in Wikipedia),CP0][CAS:100286-90-6,111348-33-5][MOA:Antineoplastic Agents Phytogenic; Enzyme Inhibitors][Indication:For the treatment of metastatic colorectal cancer (first-line therapy when administered with 5-fluorouracil and leuco ...] | 0.033500000000000002 | 0.061699999999999998 | 0.089399999999999993 | 0.039199999999999999 | 0.086800000000000002 | 0.087800000000000003 | ⋯ | 0.1764 | 0.046399999999999997 | 0.1191 | 0.1308 | 0.1338 | 0.054100000000000002 | 0.047500000000000001 | 0.035099999999999999 | 0.085099999999999995 | 0.048000000000000001 |
| GNF-00-0527-1637-7 | Topotecan | Hycamtin | [Topotecan(in Wikipedia),Sn-38(in Wikipedia)][CAS:119413-54-6,123948-87-8][MOA:Antineoplastic Agents; Enzyme Inhibitors; Antineoplastic Agents Phytogenic][Warning:Discontinued on 1993/09/15!1992/12/15!1992/02/15!1991/11/15!1991/11/15!1990/09/15; ...] | 0.023820000000000001 | 0.010449999999999999 | 0.0082799999999999992 | 0.0048900000000000002 | 0.023349999999999999 | 0.03014 | ⋯ | 0.060699999999999997 | 0.0051500000000000001 | 0.038399999999999997 | 0.044499999999999998 | 0.059200000000000003 | 0.024819999999999998 | 0.019369999999999998 | 0.02213 | 0.01821 | <0.001221 |
| GNF-00-0527-1831-7 | Cisplastin | NA | [Indication:Cisplatin is a cytotoxic whose main mode of action is formation of adducts with both nuclear and mitochondrial DNA. This involves the formation of intrastrand DNA cross-links between guanosine residues. Cisplatin is usually cell cycl ...] | >20 | >20 | >20 | >20 | >20 | >20 | ⋯ | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 |
| GNF-00-0558-2575-7 | AEW541 | NA | [Adw-742][MOA:Apoptosis-inducer; Cytostatic; Insulin-like-growth-factor-1-antagonist][Indication:Insulin like growth factor receptor type I antagonist highest phase:Phase 1 - Cancer; Discovery - Multiple myeloma][Target:Igf1r, Insulin-like growt ...] | >20 | 8.3300000000000001 | 12.609999999999999 | 10.02 | 16.25 | >20 | ⋯ | 7.4299999999999997 | 10.460000000000001 | 8.5099999999999998 | >20 | 12.199999999999999 | 9.1199999999999992 | 11.74 | 13.09 | 13.69 | >20 |
| GNF-00-0558-2578-0 | TAE684 | NA | [Nvp-tae-684][Novarits CSP/sPoC:GNF sPOC regarding ALK Inhibitor part 2 (268) approved on 2004-08-11; GNF CSP regarding ALK Inhibitor part 2 (268) approved on 2004-03-11][MOA:Alk-tyrosine-kinase-receptor-inhibitor; Apoptosis-inducer; Cytosta ...] | 4.6900000000000004 | 3.29 | 6.8899999999999997 | 1.625 | 4.0999999999999996 | 4.1900000000000004 | ⋯ | 1.3080000000000001 | 1.5249999999999999 | 0.748 | 11.31 | 2.4119999999999999 | 5.6900000000000004 | >20 | 4.6500000000000004 | 1.4530000000000001 | 5.1500000000000004 |
| GNF-00-0558-2660-3 | Panobinostat | Faridak | [Lbh-589,Panobinostat][CAS:404950-80-7][MOA:Angiogenesis-inhibitor; Apoptosis-inducer; Cytostatic; Histone-deacetylase-inhibitor; Synergist][Indication:Histone deacetylase inhibitor; Apoptosis stimulator; Cytochrome P450 2D6 inhibitor highest ph ...] | 0.01474 | 0.0039100000000000003 | 0.011780000000000001 | <0.001221 | 0.0030850000000000001 | 0.018120000000000001 | ⋯ | <0.001221 | <0.001221 | 0.0019810000000000001 | 0.0059699999999999996 | 0.0018569999999999999 | 0.0083499999999999998 | 0.03048 | <0.001221 | <0.001641 | 0.0024450000000000001 |
| GNF-00-0558-2696-5 | Erlotinib | Tarceva | [Erlotinib(in Wikipedia),Tarceva][CAS:183321-74-6,183319-69-9][MOA:Protein Kinase Inhibitors][Indication:For the treatment of patients with locally advanced or metastatic non-small cell lung cancer after failure of at least one prior chemotherap ...] | 7.1399999999999997 | >20 | 17.539999999999999 | 15.220000000000001 | 0.036299999999999999 | >20 | ⋯ | 1.609 | 11.789999999999999 | 1.514 | >20 | 4.79 | >20 | 1.1759999999999999 | 1.4139999999999999 | >20 | >20 |
| GNF-00-0558-2805-2 | PD-0332991 | NA | [Pd-0332991,6-acetyl-8-cyclopentyl-5-methyl-2-(5-piperazin-1-ylpyridin-2-ylamino)-8H-pyrido(2,3-d)pyrimidin-7-one][MOA:Cyclin-dependent-kinase-4-inhibitor; Cyclin-dependent-kinase-6-inhibitor; Cyclin-dependent-kinase-inhibitor; Cytostatic][Indic ...] | >20 | 1.962 | 0.753 | 0.093299999999999994 | 2.4249999999999998 | >20 | ⋯ | 1.4910000000000001 | 0.58099999999999996 | 1.9670000000000001 | 11.029999999999999 | 4.4000000000000004 | 0.74299999999999999 | >20 | 0.57899999999999996 | NA | 0.63700000000000001 |
| GNF-00-0558-2806-3 | Lapatinib | Tykerb | [Lapatinib(in Wikipedia),Tycerb][CAS:388082-78-8,231277-92-2][MOA:Antineoplastic Agents; Protein Kinase Inhibitors][Indication:Indicated in combination with capecitabine for the treatment of patients with advanced or metastatic breast cancer who ...] | >20 | >20 | 8.0700000000000003 | >20 | 0.35699999999999998 | >20 | ⋯ | >20 | >20 | 11.68 | >20 | 13.43 | 10.83 | >20 | >20 | >20 | >20 |
| GNF-00-0558-2811-0 | LBW242 | NA | [lbw-242][MOA:Cytostatic; Smac-protein-stimulator][Target:BIR3 domain of X-linked IAP, X-linked IAP][GNF Document:CB1\Chen_Julian-Pyridones as Novel IAP Antagonists.pdf][Patent:Organic compounds(2005); Process for preparing dipeptide amides(2006 ...] | >20 | >20 | >20 | >20 | >20 | >20 | ⋯ | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 | >20 |
| GNF-00-0558-2817-6 | RAF265 | NA | [chir-265][MOA:Angiogenesis-inhibitor; Cytostatic][Indication:VEGFR inhibitor; Raf inhibitor highest phase:Phase 1 - Skin melanoma, Cancer][Target:Raf kinase, Raf][GNF Document:B-Raf\RAF Kinase LN review-Tommasi-v2.doc, B-Raf\Raf Core Team 05090 ...] | >20 | 12.08 | 5.6100000000000003 | 0.045199999999999997 | 3.0369999999999999 | 19.98 | ⋯ | >20 | 1.7849999999999999 | 6.04 | >20 | 13.1 | 0.15870000000000001 | >20 | 0.014760000000000001 | >20 | 6.7699999999999996 |
| GNF-00-0558-2820-1 | TKI258 | Dovitinib | [CAS:405169-16-6][Indication:FLT3 inhibitor; KIT inhibitor; VEGFR antagonist; FGFR antagonist highest phase:Phase 1 - Acute myelogenous leukemia, Multiple myeloma, Solid tumor][Target:Vegfr, Fibroblast growth factor receptor 3, Protein tyrosine ...] | >20 | 2.9569999999999999 | 2.8919999999999999 | 0.21690000000000001 | 3.4500000000000002 | 2.23 | ⋯ | 2.706 | 0.71699999999999997 | 1.0529999999999999 | >20 | 4.5800000000000001 | 6.2800000000000002 | 19.48 | 1.9139999999999999 | >20 | >20 |
| GNF-00-0558-2834-7 | Vandetanib | Zactima | [Vandetanib(in Wikipedia),N-(4-bromo-2-fluorophenyl)-6-methoxy-7-((1-methylpiperidin-4-yl)methoxy)quinazolin-4-amine][CAS:443913-73-3,338992-00-0][MOA:Angiogenesis-inhibitor; Apoptosis-inducer; Cytostatic; Egf-antagonist; Epidermal-growth-factor ...] | 17.289999999999999 | 10.050000000000001 | 11.789999999999999 | 3.4399999999999999 | 1.024 | >20 | ⋯ | 14.25 | 14.99 | 5.1299999999999999 | >20 | 16.079999999999998 | 9.6500000000000004 | 13.77 | 9.6899999999999995 | 9.0199999999999996 | >20 |
| GNF-00-0558-2842-7 | 17-AAG | Tanespimycin | [17-N-Allylamino-17-demethoxygeldanamycin(in Wikipedia),Tanespimycin][CAS:75747-14-7,64202-81-9][MOA:Antibiotic; Apoptosis-inducer; Cytostatic; Hsp-90-inhibitor; Prodrug of geldanamycin; Synergist; Tyrosine-kinase-inhibitor; Androgen-antagonist; ...] | 0.032800000000000003 | 0.442 | 0.074800000000000005 | 0.01242 | 0.0407 | 0.059400000000000001 | ⋯ | 0.050500000000000003 | 0.36899999999999999 | 0.056500000000000002 | 0.0385 | 0.037400000000000003 | 0.015259999999999999 | 0.14149999999999999 | 0.01192 | 0.034500000000000003 | 0.0344 |
| GNF-00-0558-2844-9 | PD-0325901 | NA | [Pd-0325901,Pd 0325901][CAS:391210-10-9][MOA:Apoptosis-inducer; Cytostatic; Map-kinase-inhibitor; Mek-1-protein-kinase-inhibitor][Indication:MEK inhibitor; Immunosuppressant highest phase:Phase 2 - Breast tumor, Colon tumor, Melanoma, Non-small- ...] | >20 | >20 | NoFit | 0.24399999999999999 | 0.0047600000000000003 | >20 | ⋯ | <0.001244 | >20 | 3.9199999999999999 | NoFit | >20 | NoFit | >20 | <0.001221 | 5.1900000000000004 | >20 |
| GNF-00-0558-3160-2 | AZD0530 | Saracatinib | [Saracatinib,N-(5-chloro-1,3-benzodioxol-4-yl)-7-(2-(4-methylpiperazin-1-yl)ethoxy)-5-(tetrahydro-2H-pyran-4-yloxy)quinazolin-4-amine][MOA:Abl-tyrosine-kinase-inhibitor; Apoptosis-inducer; Cytostatic; Estrogen-receptor-alpha-antagonist; Src-tyro ...] | >20 | 13.300000000000001 | >20 | 5.1200000000000001 | 0.75600000000000001 | >20 | ⋯ | >20 | >20 | 0.72299999999999998 | >20 | >20 | >20 | 0.80500000000000005 | 12.83 | 12.67 | 7.2000000000000002 |
In [259]:
select_cl <- CCLE_drug %>%
filter(CCLE_drug$Target == 'EGFR') %>%
dplyr::select('CCLE Cell Line Name') %>%
t() %>% c()
In [260]:
CCLE_exp %>% dplyr::select(c('Description',select_cl))
Warning message:
“Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(select_cl)
# Now:
data %>% select(all_of(select_cl))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.”
Error in `dplyr::select()`: ! Can't subset columns that don't exist. ✖ Columns `1321N1_CENTRAL_NERVOUS_SYSTEM`, `AZ521_STOMACH`, `BGC823_STOMACH`, `C3A_LIVER`, `CHL1_SKIN`, etc. don't exist. Traceback: 1. CCLE_exp %>% dplyr::select(c("Description", select_cl)) 2. dplyr::select(., c("Description", select_cl)) 3. select.data.frame(., c("Description", select_cl)) 4. tidyselect::eval_select(expr(c(...)), data = .data, error_call = error_call) 5. eval_select_impl(data, names(data), as_quosure(expr, env), include = include, . exclude = exclude, strict = strict, name_spec = name_spec, . allow_rename = allow_rename, allow_empty = allow_empty, allow_predicates = allow_predicates, . error_call = error_call, ) 6. with_subscript_errors(out <- vars_select_eval(vars, expr, strict = strict, . data = x, name_spec = name_spec, uniquely_named = uniquely_named, . allow_rename = allow_rename, allow_empty = allow_empty, allow_predicates = allow_predicates, . type = type, error_call = error_call), type = type) 7. try_fetch(expr, vctrs_error_subscript = function(cnd) { . cnd$subscript_action <- subscript_action(type) . cnd$subscript_elt <- "column" . cnd_signal(cnd) . }) 8. withCallingHandlers(expr, condition = function(cnd) { . { . .__handler_frame__. <- TRUE . .__setup_frame__. <- frame . if (inherits(cnd, "message")) { . except <- c("warning", "error") . } . else if (inherits(cnd, "warning")) { . except <- "error" . } . else { . except <- "" . } . } . while (!is_null(cnd)) { . if (inherits(cnd, "vctrs_error_subscript")) { . out <- handlers[[1L]](cnd) . if (!inherits(out, "rlang_zap")) . throw(out) . } . inherit <- .subset2(.subset2(cnd, "rlang"), "inherit") . if (is_false(inherit)) { . return() . } . cnd <- .subset2(cnd, "parent") . } . }) 9. vars_select_eval(vars, expr, strict = strict, data = x, name_spec = name_spec, . uniquely_named = uniquely_named, allow_rename = allow_rename, . allow_empty = allow_empty, allow_predicates = allow_predicates, . type = type, error_call = error_call) 10. walk_data_tree(expr, data_mask, context_mask) 11. eval_c(expr, data_mask, context_mask) 12. reduce_sels(node, data_mask, context_mask, init = init) 13. walk_data_tree(new, data_mask, context_mask) 14. eval_c(expr, data_mask, context_mask) 15. reduce_sels(node, data_mask, context_mask, init = init) 16. walk_data_tree(new, data_mask, context_mask) 17. as_indices_sel_impl(out, vars = vars, strict = strict, data = data, . allow_predicates = allow_predicates, call = error_call, arg = as_label(expr)) 18. as_indices_impl(x, vars, call = call, arg = arg, strict = strict) 19. chr_as_locations(x, vars, call = call, arg = arg) 20. vctrs::vec_as_location(x, n = length(vars), names = vars, call = call, . arg = arg) 21. (function () . stop_subscript_oob(i = i, subscript_type = subscript_type, names = names, . subscript_action = subscript_action, subscript_arg = subscript_arg, . call = call))() 22. stop_subscript_oob(i = i, subscript_type = subscript_type, names = names, . subscript_action = subscript_action, subscript_arg = subscript_arg, . call = call) 23. stop_subscript(class = "vctrs_error_subscript_oob", i = i, subscript_type = subscript_type, . ..., call = call) 24. abort(class = c(class, "vctrs_error_subscript"), i = i, ..., . call = call) 25. signal_abort(cnd, .file) 26. signalCondition(cnd) 27. (function (cnd) . { . { . .__handler_frame__. <- TRUE . .__setup_frame__. <- frame . if (inherits(cnd, "message")) { . except <- c("warning", "error") . } . else if (inherits(cnd, "warning")) { . except <- "error" . } . else { . except <- "" . } . } . while (!is_null(cnd)) { . if (inherits(cnd, "vctrs_error_subscript")) { . out <- handlers[[1L]](cnd) . if (!inherits(out, "rlang_zap")) . throw(out) . } . inherit <- .subset2(.subset2(cnd, "rlang"), "inherit") . if (is_false(inherit)) { . return() . } . cnd <- .subset2(cnd, "parent") . } . })(structure(list(message = "", trace = structure(list(call = list( . IRkernel::main(), kernel$run(), handle_shell(), executor$execute(msg), . tryCatch(evaluate(request$content$code, envir = .GlobalEnv, . output_handler = oh, stop_on_error = 1L), interrupt = function(cond) { . log_debug("Interrupt during execution") . interrupted <<- TRUE . }, error = .self$handle_error), tryCatchList(expr, classes, . parentenv, handlers), tryCatchOne(tryCatchList(expr, . names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, . handlers[[nh]]), doTryCatch(return(expr), name, parentenv, . handler), tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), . tryCatchOne(expr, names, parentenv, handlers[[1L]]), doTryCatch(return(expr), . name, parentenv, handler), evaluate(request$content$code, . envir = .GlobalEnv, output_handler = oh, stop_on_error = 1L), . evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, . debug = debug, last = i == length(out), use_try = stop_on_error != . 2L, keep_warning = keep_warning, keep_message = keep_message, . log_echo = log_echo, log_warning = log_warning, output_handler = output_handler, . include_timing = include_timing), timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, . envir, enclos, user_handlers)), warning = wHandler, error = eHandler, . message = mHandler))), handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, . envir, enclos, user_handlers)), warning = wHandler, error = eHandler, . message = mHandler)), try(f, silent = TRUE), tryCatch(expr, . error = function(e) { . call <- conditionCall(e) . if (!is.null(call)) { . if (identical(call[[1L]], quote(doTryCatch))) . call <- sys.call(-4L) . dcall <- deparse(call, nlines = 1L) . prefix <- paste("Error in", dcall, ": ") . LONG <- 75L . sm <- strsplit(conditionMessage(e), "\n")[[1L]] . w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], . type = "w") . if (is.na(w)) . w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], . type = "b") . if (w > LONG) . prefix <- paste0(prefix, "\n ") . } . else prefix <- "Error : " . msg <- paste0(prefix, conditionMessage(e), "\n") . .Internal(seterrmessage(msg[1L])) . if (!silent && isTRUE(getOption("show.error.messages"))) { . cat(msg, file = outFile) . .Internal(printDeferredWarnings()) . } . invisible(structure(msg, class = "try-error", condition = e)) . }), tryCatchList(expr, classes, parentenv, handlers), . tryCatchOne(expr, names, parentenv, handlers[[1L]]), doTryCatch(return(expr), . name, parentenv, handler), withCallingHandlers(withVisible(eval_with_user_handlers(expr, . envir, enclos, user_handlers)), warning = wHandler, error = eHandler, . message = mHandler), withVisible(eval_with_user_handlers(expr, . envir, enclos, user_handlers)), eval_with_user_handlers(expr, . envir, enclos, user_handlers), eval(expr, envir, enclos), . eval(expr, envir, enclos), CCLE_exp %>% dplyr::select(c("Description", . select_cl)), dplyr::select(., c("Description", select_cl)), . select.data.frame(., c("Description", select_cl)), tidyselect::eval_select(expr(c(...)), . data = .data, error_call = error_call), eval_select_impl(data, . names(data), as_quosure(expr, env), include = include, . exclude = exclude, strict = strict, name_spec = name_spec, . allow_rename = allow_rename, allow_empty = allow_empty, . allow_predicates = allow_predicates, error_call = error_call, . ), with_subscript_errors(out <- vars_select_eval(vars, . expr, strict = strict, data = x, name_spec = name_spec, . uniquely_named = uniquely_named, allow_rename = allow_rename, . allow_empty = allow_empty, allow_predicates = allow_predicates, . type = type, error_call = error_call), type = type), . try_fetch(expr, vctrs_error_subscript = function(cnd) { . cnd$subscript_action <- subscript_action(type) . cnd$subscript_elt <- "column" . cnd_signal(cnd) . }), withCallingHandlers(expr, condition = function(cnd) { . { . .__handler_frame__. <- TRUE . .__setup_frame__. <- frame . if (inherits(cnd, "message")) { . except <- c("warning", "error") . } . else if (inherits(cnd, "warning")) { . except <- "error" . } . else { . except <- "" . } . } . while (!is_null(cnd)) { . if (inherits(cnd, "vctrs_error_subscript")) { . out <- handlers[[1L]](cnd) . if (!inherits(out, "rlang_zap")) . throw(out) . } . inherit <- .subset2(.subset2(cnd, "rlang"), "inherit") . if (is_false(inherit)) { . return() . } . cnd <- .subset2(cnd, "parent") . } . }), vars_select_eval(vars, expr, strict = strict, data = x, . name_spec = name_spec, uniquely_named = uniquely_named, . allow_rename = allow_rename, allow_empty = allow_empty, . allow_predicates = allow_predicates, type = type, error_call = error_call), . walk_data_tree(expr, data_mask, context_mask), eval_c(expr, . data_mask, context_mask), reduce_sels(node, data_mask, . context_mask, init = init), walk_data_tree(new, data_mask, . context_mask), eval_c(expr, data_mask, context_mask), . reduce_sels(node, data_mask, context_mask, init = init), . walk_data_tree(new, data_mask, context_mask), as_indices_sel_impl(out, . vars = vars, strict = strict, data = data, allow_predicates = allow_predicates, . call = error_call, arg = as_label(expr)), as_indices_impl(x, . vars, call = call, arg = arg, strict = strict), chr_as_locations(x, . vars, call = call, arg = arg), vctrs::vec_as_location(x, . n = length(vars), names = vars, call = call, arg = arg), . `<fn>`(), stop_subscript_oob(i = i, subscript_type = subscript_type, . names = names, subscript_action = subscript_action, subscript_arg = subscript_arg, . call = call), stop_subscript(class = "vctrs_error_subscript_oob", . i = i, subscript_type = subscript_type, ..., call = call), . abort(class = c(class, "vctrs_error_subscript"), i = i, ..., . call = call)), parent = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, . 7L, 6L, 9L, 10L, 4L, 12L, 13L, 13L, 15L, 16L, 17L, 18L, 19L, . 13L, 13L, 13L, 23L, 24L, 0L, 0L, 0L, 28L, 29L, 30L, 31L, 32L, . 30L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 0L, . 46L, 47L, 48L), visible = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, . TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, . TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, . FALSE, FALSE, FALSE), namespace = c("IRkernel", NA, "IRkernel", . NA, "base", "base", "base", "base", "base", "base", "base", "evaluate", . "evaluate", "evaluate", "evaluate", "base", "base", "base", "base", . "base", "base", "base", "evaluate", "base", "base", NA, "dplyr", . "dplyr", "tidyselect", "tidyselect", "tidyselect", "rlang", "base", . "tidyselect", "tidyselect", "tidyselect", "tidyselect", "tidyselect", . "tidyselect", "tidyselect", "tidyselect", "tidyselect", "tidyselect", . "tidyselect", "vctrs", "vctrs", "vctrs", "vctrs", "rlang"), scope = c("::", . NA, "local", NA, "::", "local", "local", "local", "local", "local", . "local", "::", ":::", "local", "local", "::", "::", "local", . "local", "local", "::", "::", ":::", "::", "::", NA, "::", ":::", . "::", ":::", ":::", "::", "::", ":::", ":::", ":::", ":::", ":::", . ":::", ":::", ":::", ":::", ":::", ":::", "::", "local", ":::", . ":::", "::"), error_frame = c(FALSE, FALSE, FALSE, FALSE, FALSE, . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, . FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, . FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)), row.names = c(NA, . -49L), version = 2L, class = c("rlang_trace", "rlib_trace", "tbl", . "data.frame")), parent = NULL, i = c("1321N1_CENTRAL_NERVOUS_SYSTEM", . "22RV1_PROSTATE", "42MGBA_CENTRAL_NERVOUS_SYSTEM", "5637_URINARY_TRACT", . "639V_URINARY_TRACT", "697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "769P_KIDNEY", "786O_KIDNEY", "8305C_THYROID", "8505C_THYROID", . "8MGBA_CENTRAL_NERVOUS_SYSTEM", "A172_CENTRAL_NERVOUS_SYSTEM", . "A204_SOFT_TISSUE", "A2058_SKIN", "A253_SALIVARY_GLAND", "A2780_OVARY", . "A375_SKIN", "A549_LUNG", "A673_BONE", "ACHN_KIDNEY", "ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "AN3CA_ENDOMETRIUM", . "ASPC1_PANCREAS", "AU565_BREAST", "AZ521_STOMACH", "BCPAP_THYROID", . "BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BFTC909_KIDNEY", . "BGC823_STOMACH", "BL41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "BL70_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BT20_BREAST", "BT474_BREAST", . "BT549_BREAST", "BXPC3_PANCREAS", "C2BBE1_LARGE_INTESTINE", "C32_SKIN", . "C3A_LIVER", "CAKI2_KIDNEY", "CAL12T_LUNG", "CAL27_UPPER_AERODIGESTIVE_TRACT", . "CAL78_BONE", "CAL851_BREAST", "CALU1_LUNG", "CALU3_LUNG", "CALU6_LUNG", . "CAMA1_BREAST", "CAPAN2_PANCREAS", "CAS1_CENTRAL_NERVOUS_SYSTEM", . "CCK81_LARGE_INTESTINE", "CHL1_SKIN", "CHP212_AUTONOMIC_GANGLIA", . "CI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "CMK86_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "COLO201_LARGE_INTESTINE", "COLO205_LARGE_INTESTINE", "COLO320_LARGE_INTESTINE", . "COLO677_LUNG", "COLO678_LARGE_INTESTINE", "COLO679_SKIN", "COLO699_LUNG", . "COLO741_SKIN", "CORL105_LUNG", "CORL23_LUNG", "COV318_OVARY", . "COV504_OVARY", "DAOY_CENTRAL_NERVOUS_SYSTEM", "DBTRG05MG_CENTRAL_NERVOUS_SYSTEM", . "DEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DETROIT562_UPPER_AERODIGESTIVE_TRACT", . "DKMG_CENTRAL_NERVOUS_SYSTEM", "DMS114_LUNG", "DOHH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "DOV13_OVARY", "DU145_PROSTATE", "DV90_LUNG", "EB1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EB2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "EBC1_LUNG", "EFE184_ENDOMETRIUM", . "EFM19_BREAST", "EFO21_OVARY", "EFO27_OVARY", "EM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EN_ENDOMETRIUM", "ES2_OVARY", "ESS1_ENDOMETRIUM", "F36P_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "FADU_UPPER_AERODIGESTIVE_TRACT", "FU97_STOMACH", "FUOV1_OVARY", . "G361_SKIN", "G401_SOFT_TISSUE", "G402_SOFT_TISSUE", "GAMG_CENTRAL_NERVOUS_SYSTEM", . "GB1_CENTRAL_NERVOUS_SYSTEM", "GCIY_STOMACH", "GCT_SOFT_TISSUE", . "GI1_CENTRAL_NERVOUS_SYSTEM", "GLC82_LUNG", "GMS10_CENTRAL_NERVOUS_SYSTEM", . "GRANTA519_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "H4_CENTRAL_NERVOUS_SYSTEM", . "HARA_LUNG", "HCC1187_BREAST", "HCC1395_BREAST", "HCC15_LUNG", . "HCC1569_BREAST", "HCC1806_BREAST", "HCC1954_BREAST", "HCC2935_LUNG", . "HCC4006_LUNG", "HCC44_LUNG", "HCC56_LARGE_INTESTINE", "HCC70_BREAST", . "HCC78_LUNG", "HCC827_LUNG", "HCT116_LARGE_INTESTINE", "HCT15_LARGE_INTESTINE", . "HDMYZ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HDQP1_BREAST", "HEC151_ENDOMETRIUM", . "HEC1A_ENDOMETRIUM", "HEC1B_ENDOMETRIUM", "HEC251_ENDOMETRIUM", . "HEC265_ENDOMETRIUM", "HEC59_ENDOMETRIUM", "HEC6_ENDOMETRIUM", . "HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HEP3B217_LIVER", . "HEPG2_LIVER", "HEYA8_OVARY", "HGC27_STOMACH", "HH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HLE_LIVER", "HLF_LIVER", "HMC18_BREAST", "HMCB_SKIN", "HOS_BONE", . "HPAC_PANCREAS", "HPAFII_PANCREAS", "HS229T_LUNG", "HS294T_SKIN", . "HS578T_BREAST", "HS683_CENTRAL_NERVOUS_SYSTEM", "HS695T_SKIN", . "HS729_SOFT_TISSUE", "HS739T_BREAST", "HS746T_STOMACH", "HS766T_PANCREAS", . "HS840T_UPPER_AERODIGESTIVE_TRACT", "HS852T_SKIN", "HS895T_SKIN", . "HS936T_SKIN", "HS939T_SKIN", "HS944T_SKIN", "HSC2_UPPER_AERODIGESTIVE_TRACT", . "HT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HT1080_SOFT_TISSUE", . "HT1197_URINARY_TRACT", "HT1376_URINARY_TRACT", "HT144_SKIN", . "HT29_LARGE_INTESTINE", "HUCCT1_BILIARY_TRACT", "HUH1_LIVER", . "HUPT3_PANCREAS", "HUPT4_PANCREAS", "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "IALM_LUNG", "IGR37_SKIN", "IGR39_SKIN", "IGROV1_OVARY", "IM95_STOMACH", . "IMR32_AUTONOMIC_GANGLIA", "IPC298_SKIN", "ISHIKAWAHERAKLIO02ER_ENDOMETRIUM", . "ISTMES1_PLEURA", "ISTMES2_PLEURA", "J82_URINARY_TRACT", "JHH2_LIVER", . "JHH4_LIVER", "JHH5_LIVER", "JHH6_LIVER", "JHH7_LIVER", "JHOS2_OVARY", . "JHOS4_OVARY", "JHUEM2_ENDOMETRIUM", "JM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JMSU1_URINARY_TRACT", "JURKAT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JVM3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "K029AX_SKIN", "KALS1_CENTRAL_NERVOUS_SYSTEM", . "KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KARPAS620_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KCIMOH1_PANCREAS", "KCL22_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KE39_STOMACH", "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KELLY_AUTONOMIC_GANGLIA", . "KG1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KHM1B_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KLE_ENDOMETRIUM", "KMBC2_URINARY_TRACT", "KMM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMRC1_KIDNEY", "KMRC2_KIDNEY", "KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS12BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KNS42_CENTRAL_NERVOUS_SYSTEM", . "KNS60_CENTRAL_NERVOUS_SYSTEM", "KNS62_LUNG", "KNS81_CENTRAL_NERVOUS_SYSTEM", . "KO52_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KP1N_PANCREAS", "KP1NL_PANCREAS", . "KP2_PANCREAS", "KP3_PANCREAS", "KP4_PANCREAS", "KPNSI9S_AUTONOMIC_GANGLIA", . "KU812_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KURAMOCHI_OVARY", . "KYM1_SOFT_TISSUE", "KYSE140_OESOPHAGUS", "KYSE150_OESOPHAGUS", . "KYSE180_OESOPHAGUS", "KYSE30_OESOPHAGUS", "KYSE410_OESOPHAGUS", . "KYSE450_OESOPHAGUS", "KYSE510_OESOPHAGUS", "KYSE520_OESOPHAGUS", . "KYSE70_OESOPHAGUS", "L33_PANCREAS", "L363_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "L428_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LC1SQSF_LUNG", "LCLC103H_LUNG", . "LN18_CENTRAL_NERVOUS_SYSTEM", "LN229_CENTRAL_NERVOUS_SYSTEM", . "LOUNH91_LUNG", "LOXIMVI_SKIN", "LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "LS123_LARGE_INTESTINE", "LS411N_LARGE_INTESTINE", "LS513_LARGE_INTESTINE", . "LU99_LUNG", "LUDLU1_LUNG", "M059J", "MALME3M_SKIN", "MB157_BREAST", . "MC116_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MCAS_OVARY", "MCF7_BREAST", . "MDAMB157_BREAST", "MDAMB175VII_BREAST", "MDAMB415_BREAST", "MDAMB435S_SKIN", . "MDAMB436_BREAST", "MDAMB453_BREAST", "MDAMB468_BREAST", "MEC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MEG01_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MELHO_SKIN", "MESSA_SOFT_TISSUE", . "MEWO_SKIN", "MFE280_ENDOMETRIUM", "MFE296_ENDOMETRIUM", "MFE319_ENDOMETRIUM", . "MG63_BONE", "MHHES1_BONE", "MIAPACA2_PANCREAS", "MINO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MKN45_STOMACH", "MKN7_STOMACH", . "MKN74_STOMACH", "MOGGCCM_CENTRAL_NERVOUS_SYSTEM", "MOLP8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MOLT16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MONOMAC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MORCPR_LUNG", "MPP89_PLEURA", "MSTO211H_PLEURA", "NCIH1048_LUNG", . "NCIH1092_LUNG", "NCIH1155_LUNG", "NCIH1184_LUNG", "NCIH1299_LUNG", . "NCIH1339_LUNG", "NCIH1341_LUNG", "NCIH1355_LUNG", "NCIH1373_LUNG", . "NCIH1563_LUNG", "NCIH1568_LUNG", "NCIH1573_LUNG", "NCIH1581_LUNG", . "NCIH1648_LUNG", "NCIH1650_LUNG", "NCIH1651_LUNG", "NCIH1666_LUNG", . "NCIH1693_LUNG", "NCIH1694_LUNG", "NCIH1703_LUNG", "NCIH1792_LUNG", . "NCIH1793_LUNG", "NCIH1869_LUNG", "NCIH1915_LUNG", "NCIH1944_LUNG", . "NCIH1975_LUNG", "NCIH2009_LUNG", "NCIH2023_LUNG", "NCIH2030_LUNG", . "NCIH2052_PLEURA", "NCIH2085_LUNG", "NCIH2087_LUNG", "NCIH211_LUNG", . "NCIH2122_LUNG", "NCIH2170_LUNG", "NCIH2172_LUNG", "NCIH2228_LUNG", . "NCIH226_LUNG", "NCIH2286_LUNG", "NCIH23_LUNG", "NCIH2444_LUNG", . "NCIH2452_PLEURA", "NCIH28_PLEURA", "NCIH322_LUNG", "NCIH3255_LUNG", . "NCIH358_LUNG", "NCIH441_LUNG", "NCIH460_LUNG", "NCIH520_LUNG", . "NCIH522_LUNG", "NCIH647_LUNG", "NCIH650_LUNG", "NCIH661_LUNG", . "NCIH727_LUNG", "NCIH747_LARGE_INTESTINE", "NCIH810_LUNG", "NCIN87_STOMACH", . "NCO2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NIHOVCAR3_OVARY", . "NUGC3_STOMACH", "NUGC4_STOMACH", "OC314_OVARY", "OC315_OVARY", . "OC316_OVARY", "OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCIAML5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCILY10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCUM1_STOMACH", "OE21_OESOPHAGUS", "OE33_OESOPHAGUS", "ONCODG1_OVARY", . "ONS76_CENTRAL_NERVOUS_SYSTEM", "OPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OV90_OVARY", "OVCAR4_OVARY", "OVCAR8_OVARY", "OVMANA_OVARY", . "OVSAHO_OVARY", "OVTOKO_OVARY", "P12ICHIKAWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "PANC0203_PANCREAS", "PANC0327_PANCREAS", "PANC0403_PANCREAS", . "PANC1005_PANCREAS", "PATU8902_PANCREAS", "PC14_LUNG", "PC3_PROSTATE", . "PFEIFFER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PK1_PANCREAS", . "PK45H_PANCREAS", "PK59_PANCREAS", "PL45_PANCREAS", "PLCPRF5_LIVER", . "PSN1_PANCREAS", "QGP1_PANCREAS", "RAJI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RD_SOFT_TISSUE", "REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RERFGC1B_STOMACH", . "RERFLCAI_LUNG", "RERFLCMS_LUNG", "RKN_OVARY", "RKO_LARGE_INTESTINE", . "RL952_ENDOMETRIUM", "RPMI7951_SKIN", "RPMI8402_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RT112_URINARY_TRACT", "RT4_URINARY_TRACT", "RVH421_SKIN", "SAOS2_BONE", . "SBC5_LUNG", "SCABER_URINARY_TRACT", "SCC25_UPPER_AERODIGESTIVE_TRACT", . "SCC9_UPPER_AERODIGESTIVE_TRACT", "SF126_CENTRAL_NERVOUS_SYSTEM", . "SF295_CENTRAL_NERVOUS_SYSTEM", "SF8657", "SH10TC_STOMACH", "SHP77_LUNG", . "SIGM5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SIMA_AUTONOMIC_GANGLIA", . "SJRH30_SOFT_TISSUE", "SJSA1_BONE", "SKBR3_BREAST", "SKCO1_LARGE_INTESTINE", . "SKES1_BONE", "SKHEP1_LIVER", "SKLMS1_SOFT_TISSUE", "SKLU1_LUNG", . "SKMEL2_SKIN", "SKMEL24_SKIN", "SKMEL30_SKIN", "SKMEL31_SKIN", . "SKMEL5_SKIN", "SKMES1_LUNG", "SKMM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SKNAS_AUTONOMIC_GANGLIA", "SKNBE2_AUTONOMIC_GANGLIA", "SKNDZ_AUTONOMIC_GANGLIA", . "SKNFI_AUTONOMIC_GANGLIA", "SKNSH_AUTONOMIC_GANGLIA", "SKOV3_OVARY", . "SNGM_ENDOMETRIUM", "SNU1_STOMACH", "SNU16_STOMACH", "SNU182_LIVER", . "SNU387_LIVER", "SNU398_LIVER", "SNU423_LIVER", "SNU449_LIVER", . "SNU475_LIVER", "SNUC2A_LARGE_INTESTINE", "SNUC2B", "SQ1_LUNG", . "SU8686_PANCREAS", "SUDHL10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUDHL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUDHL8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUIT2_PANCREAS", . "SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SW1088_CENTRAL_NERVOUS_SYSTEM", "SW1271_LUNG", "SW1353_BONE", . "SW1417_LARGE_INTESTINE", "SW1573_LUNG", "SW1990_PANCREAS", "SW403_LARGE_INTESTINE", . "SW48_LARGE_INTESTINE", "SW480_LARGE_INTESTINE", "SW579_THYROID", . "SW620_LARGE_INTESTINE", "SW780_URINARY_TRACT", "SW900_LUNG", . "T24_URINARY_TRACT", "T3M10_LUNG", "T47D_BREAST", "T84_LARGE_INTESTINE", . "T98G_CENTRAL_NERVOUS_SYSTEM", "TC71_BONE", "TCCSUP_URINARY_TRACT", . "TE1_OESOPHAGUS", "TE11_OESOPHAGUS", "TE15_OESOPHAGUS", "TE5_OESOPHAGUS", . "TE617T_SOFT_TISSUE", "TE9_OESOPHAGUS", "TEN_ENDOMETRIUM", "TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "TOV112D_OVARY", "TOV21G_OVARY", "TT2609C02_THYROID", "TYKNU_OVARY", . "U118MG_CENTRAL_NERVOUS_SYSTEM", "U2OS_BONE", "U87MG_CENTRAL_NERVOUS_SYSTEM", . "U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "UACC257_SKIN", "UACC62_SKIN", . "UACC812_BREAST", "UMUC3_URINARY_TRACT", "VMRCLCD_LUNG", "VMRCRCW_KIDNEY", . "VMRCRCZ_KIDNEY", "WM115_SKIN", "WM1799_SKIN", "WM2664_SKIN", . "WM793_SKIN", "WM88_SKIN", "WM983B_SKIN", "YKG1_CENTRAL_NERVOUS_SYSTEM", . "ZR751_BREAST", "ZR7530_BREAST", "1321N1_CENTRAL_NERVOUS_SYSTEM", . "22RV1_PROSTATE", "42MGBA_CENTRAL_NERVOUS_SYSTEM", "5637_URINARY_TRACT", . "639V_URINARY_TRACT", "697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "769P_KIDNEY", "8305C_THYROID", "8505C_THYROID", "8MGBA_CENTRAL_NERVOUS_SYSTEM", . "A172_CENTRAL_NERVOUS_SYSTEM", "A204_SOFT_TISSUE", "A2058_SKIN", . "A253_SALIVARY_GLAND", "A2780_OVARY", "A375_SKIN", "A549_LUNG", . "A673_BONE", "ACHN_KIDNEY", "ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "AN3CA_ENDOMETRIUM", . "ASPC1_PANCREAS", "AU565_BREAST", "AZ521_STOMACH", "BCPAP_THYROID", . "BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BFTC909_KIDNEY", . "BGC823_STOMACH", "BL41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "BL70_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BT20_BREAST", "BT474_BREAST", . "BT549_BREAST", "BXPC3_PANCREAS", "C2BBE1_LARGE_INTESTINE", "C32_SKIN", . "C3A_LIVER", "CAKI2_KIDNEY", "CAL12T_LUNG", "CAL27_UPPER_AERODIGESTIVE_TRACT", . "CAL78_BONE", "CAL851_BREAST", "CALU1_LUNG", "CALU3_LUNG", "CALU6_LUNG", . "CAMA1_BREAST", "CAPAN2_PANCREAS", "CAS1_CENTRAL_NERVOUS_SYSTEM", . "CCK81_LARGE_INTESTINE", "CHL1_SKIN", "CHP212_AUTONOMIC_GANGLIA", . "CI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "CMK86_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "COLO201_LARGE_INTESTINE", "COLO205_LARGE_INTESTINE", "COLO320_LARGE_INTESTINE", . "COLO677_LUNG", "COLO678_LARGE_INTESTINE", "COLO679_SKIN", "COLO699_LUNG", . "COLO741_SKIN", "CORL105_LUNG", "CORL23_LUNG", "COV318_OVARY", . "COV504_OVARY", "DAOY_CENTRAL_NERVOUS_SYSTEM", "DBTRG05MG_CENTRAL_NERVOUS_SYSTEM", . "DEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DETROIT562_UPPER_AERODIGESTIVE_TRACT", . "DKMG_CENTRAL_NERVOUS_SYSTEM", "DMS114_LUNG", "DOHH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "DOV13_OVARY", "DU145_PROSTATE", "DV90_LUNG", "EB1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EB2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "EBC1_LUNG", "EFE184_ENDOMETRIUM", . "EFM19_BREAST", "EFO21_OVARY", "EM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EN_ENDOMETRIUM", "ES2_OVARY", "ESS1_ENDOMETRIUM", "F36P_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "FADU_UPPER_AERODIGESTIVE_TRACT", "FU97_STOMACH", "FUOV1_OVARY", . "G361_SKIN", "G401_SOFT_TISSUE", "G402_SOFT_TISSUE", "GAMG_CENTRAL_NERVOUS_SYSTEM", . "GB1_CENTRAL_NERVOUS_SYSTEM", "GCIY_STOMACH", "GCT_SOFT_TISSUE", . "GI1_CENTRAL_NERVOUS_SYSTEM", "GLC82_LUNG", "GMS10_CENTRAL_NERVOUS_SYSTEM", . "GRANTA519_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "H4_CENTRAL_NERVOUS_SYSTEM", . "HARA_LUNG", "HCC1187_BREAST", "HCC1395_BREAST", "HCC15_LUNG", . "HCC1569_BREAST", "HCC1806_BREAST", "HCC1954_BREAST", "HCC2935_LUNG", . "HCC4006_LUNG", "HCC44_LUNG", "HCC56_LARGE_INTESTINE", "HCC70_BREAST", . "HCC78_LUNG", "HCC827_LUNG", "HCT116_LARGE_INTESTINE", "HCT15_LARGE_INTESTINE", . "HDMYZ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HDQP1_BREAST", "HEC151_ENDOMETRIUM", . "HEC1A_ENDOMETRIUM", "HEC1B_ENDOMETRIUM", "HEC251_ENDOMETRIUM", . "HEC265_ENDOMETRIUM", "HEC59_ENDOMETRIUM", "HEC6_ENDOMETRIUM", . "HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HEP3B217_LIVER", . "HEPG2_LIVER", "HGC27_STOMACH", "HH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HLE_LIVER", "HLF_LIVER", "HMC18_BREAST", "HMCB_SKIN", "HOS_BONE", . "HPAC_PANCREAS", "HPAFII_PANCREAS", "HS229T_LUNG", "HS294T_SKIN", . "HS578T_BREAST", "HS683_CENTRAL_NERVOUS_SYSTEM", "HS695T_SKIN", . "HS729_SOFT_TISSUE", "HS739T_BREAST", "HS746T_STOMACH", "HS766T_PANCREAS", . "HS840T_UPPER_AERODIGESTIVE_TRACT", "HS852T_SKIN", "HS895T_SKIN", . "HS936T_SKIN", "HS939T_SKIN", "HS944T_SKIN", "HSC2_UPPER_AERODIGESTIVE_TRACT", . "HT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HT1080_SOFT_TISSUE", . "HT1197_URINARY_TRACT", "HT1376_URINARY_TRACT", "HT144_SKIN", . "HT29_LARGE_INTESTINE", "HUCCT1_BILIARY_TRACT", "HUH1_LIVER", . "HUPT3_PANCREAS", "HUPT4_PANCREAS", "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "IALM_LUNG", "IGR37_SKIN", "IGR39_SKIN", "IM95_STOMACH", "IMR32_AUTONOMIC_GANGLIA", . "IPC298_SKIN", "ISHIKAWAHERAKLIO02ER_ENDOMETRIUM", "ISTMES1_PLEURA", . "ISTMES2_PLEURA", "J82_URINARY_TRACT", "JHH2_LIVER", "JHH4_LIVER", . "JHH5_LIVER", "JHH6_LIVER", "JHH7_LIVER", "JHOS2_OVARY", "JHOS4_OVARY", . "JHUEM2_ENDOMETRIUM", "JM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JMSU1_URINARY_TRACT", "JURKAT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JVM3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "K029AX_SKIN", "KALS1_CENTRAL_NERVOUS_SYSTEM", . "KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KARPAS620_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KCIMOH1_PANCREAS", "KCL22_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KE39_STOMACH", "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KELLY_AUTONOMIC_GANGLIA", . "KG1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KHM1B_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KLE_ENDOMETRIUM", "KMBC2_URINARY_TRACT", "KMM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMRC1_KIDNEY", "KMRC2_KIDNEY", "KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS12BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KNS42_CENTRAL_NERVOUS_SYSTEM", . "KNS60_CENTRAL_NERVOUS_SYSTEM", "KNS62_LUNG", "KNS81_CENTRAL_NERVOUS_SYSTEM", . "KO52_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KP1N_PANCREAS", "KP1NL_PANCREAS", . "KP3_PANCREAS", "KP4_PANCREAS", "KPNSI9S_AUTONOMIC_GANGLIA", . "KU812_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KURAMOCHI_OVARY", . "KYM1_SOFT_TISSUE", "KYSE140_OESOPHAGUS", "KYSE150_OESOPHAGUS", . "KYSE180_OESOPHAGUS", "KYSE30_OESOPHAGUS", "KYSE410_OESOPHAGUS", . "KYSE450_OESOPHAGUS", "KYSE510_OESOPHAGUS", "KYSE520_OESOPHAGUS", . "KYSE70_OESOPHAGUS", "L33_PANCREAS", "L363_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "L428_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LC1SQSF_LUNG", "LCLC103H_LUNG", . "LN229_CENTRAL_NERVOUS_SYSTEM", "LOUNH91_LUNG", "LOXIMVI_SKIN", . "LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LS123_LARGE_INTESTINE", . "LS411N_LARGE_INTESTINE", "LS513_LARGE_INTESTINE", "LU99_LUNG", . "LUDLU1_LUNG", "M059J", "MALME3M_SKIN", "MB157_BREAST", "MC116_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MCAS_OVARY", "MCF7_BREAST", "MDAMB157_BREAST", "MDAMB175VII_BREAST", . "MDAMB415_BREAST", "MDAMB435S_SKIN", "MDAMB436_BREAST", "MDAMB453_BREAST", . "MDAMB468_BREAST", "MEC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MEG01_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MELHO_SKIN", "MESSA_SOFT_TISSUE", . "MEWO_SKIN", "MFE280_ENDOMETRIUM", "MFE296_ENDOMETRIUM", "MFE319_ENDOMETRIUM", . "MG63_BONE", "MHHES1_BONE", "MIAPACA2_PANCREAS", "MINO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MKN45_STOMACH", "MKN7_STOMACH", . "MKN74_STOMACH", "MOGGCCM_CENTRAL_NERVOUS_SYSTEM", "MOLP8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MOLT16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MONOMAC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MORCPR_LUNG", "MPP89_PLEURA", "MSTO211H_PLEURA", "NCIH1048_LUNG", . "NCIH1092_LUNG", "NCIH1155_LUNG", "NCIH1184_LUNG", "NCIH1299_LUNG", . "NCIH1339_LUNG", "NCIH1341_LUNG", "NCIH1355_LUNG", "NCIH1373_LUNG", . "NCIH1563_LUNG", "NCIH1568_LUNG", "NCIH1573_LUNG", "NCIH1581_LUNG", . "NCIH1648_LUNG", "NCIH1650_LUNG", "NCIH1651_LUNG", "NCIH1666_LUNG", . "NCIH1693_LUNG", "NCIH1694_LUNG", "NCIH1703_LUNG", "NCIH1792_LUNG", . "NCIH1793_LUNG", "NCIH1869_LUNG", "NCIH1915_LUNG", "NCIH1944_LUNG", . "NCIH1975_LUNG", "NCIH2009_LUNG", "NCIH2023_LUNG", "NCIH2030_LUNG", . "NCIH2052_PLEURA", "NCIH2085_LUNG", "NCIH2087_LUNG", "NCIH211_LUNG", . "NCIH2122_LUNG", "NCIH2170_LUNG", "NCIH2172_LUNG", "NCIH2228_LUNG", . "NCIH226_LUNG", "NCIH2286_LUNG", "NCIH23_LUNG", "NCIH2444_LUNG", . "NCIH2452_PLEURA", "NCIH28_PLEURA", "NCIH322_LUNG", "NCIH3255_LUNG", . "NCIH358_LUNG", "NCIH441_LUNG", "NCIH460_LUNG", "NCIH520_LUNG", . "NCIH522_LUNG", "NCIH647_LUNG", "NCIH650_LUNG", "NCIH661_LUNG", . "NCIH727_LUNG", "NCIH747_LARGE_INTESTINE", "NCIH810_LUNG", "NCIN87_STOMACH", . "NCO2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NIHOVCAR3_OVARY", . "NUGC3_STOMACH", "NUGC4_STOMACH", "OC314_OVARY", "OC315_OVARY", . "OC316_OVARY", "OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCIAML5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCILY10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCUM1_STOMACH", "OE21_OESOPHAGUS", "OE33_OESOPHAGUS", "ONCODG1_OVARY", . "ONS76_CENTRAL_NERVOUS_SYSTEM", "OPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OV90_OVARY", "OVCAR4_OVARY", "OVCAR8_OVARY", "OVMANA_OVARY", . "OVSAHO_OVARY", "P12ICHIKAWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "PANC0203_PANCREAS", "PANC0327_PANCREAS", "PANC0403_PANCREAS", . "PANC1005_PANCREAS", "PATU8902_PANCREAS", "PC14_LUNG", "PC3_PROSTATE", . "PFEIFFER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PK1_PANCREAS", . "PK45H_PANCREAS", "PK59_PANCREAS", "PL45_PANCREAS", "PLCPRF5_LIVER", . "PSN1_PANCREAS", "QGP1_PANCREAS", "RAJI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RD_SOFT_TISSUE", "REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RERFGC1B_STOMACH", . "RERFLCAI_LUNG", "RERFLCMS_LUNG", "RKN_OVARY", "RKO_LARGE_INTESTINE", . "RL952_ENDOMETRIUM", "RPMI7951_SKIN", "RPMI8402_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RT112_URINARY_TRACT", "RT4_URINARY_TRACT", "RVH421_SKIN", "SAOS2_BONE", . "SBC5_LUNG", "SCABER_URINARY_TRACT", "SCC25_UPPER_AERODIGESTIVE_TRACT", . "SCC9_UPPER_AERODIGESTIVE_TRACT", "SF126_CENTRAL_NERVOUS_SYSTEM", . "SF295_CENTRAL_NERVOUS_SYSTEM", "SF8657", "SH10TC_STOMACH", "SHP77_LUNG", . "SIGM5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SIMA_AUTONOMIC_GANGLIA", . "SJRH30_SOFT_TISSUE", "SJSA1_BONE", "SKBR3_BREAST", "SKCO1_LARGE_INTESTINE", . "SKES1_BONE", "SKHEP1_LIVER", "SKLMS1_SOFT_TISSUE", "SKLU1_LUNG", . "SKMEL2_SKIN", "SKMEL24_SKIN", "SKMEL30_SKIN", "SKMEL31_SKIN", . "SKMEL5_SKIN", "SKMES1_LUNG", "SKMM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SKNAS_AUTONOMIC_GANGLIA", "SKNBE2_AUTONOMIC_GANGLIA", "SKNDZ_AUTONOMIC_GANGLIA", . "SKNFI_AUTONOMIC_GANGLIA", "SKNSH_AUTONOMIC_GANGLIA", "SKOV3_OVARY", . "SNGM_ENDOMETRIUM", "SNU1_STOMACH", "SNU16_STOMACH", "SNU182_LIVER", . "SNU387_LIVER", "SNU398_LIVER", "SNU423_LIVER", "SNU449_LIVER", . "SNU475_LIVER", "SNUC2A_LARGE_INTESTINE", "SNUC2B", "SQ1_LUNG", . "SU8686_PANCREAS", "SUDHL10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUDHL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUDHL8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUIT2_PANCREAS", . "SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SW1088_CENTRAL_NERVOUS_SYSTEM", "SW1271_LUNG", "SW1353_BONE", . "SW1417_LARGE_INTESTINE", "SW1573_LUNG", "SW1990_PANCREAS", "SW403_LARGE_INTESTINE", . "SW48_LARGE_INTESTINE", "SW480_LARGE_INTESTINE", "SW579_THYROID", . "SW620_LARGE_INTESTINE", "SW780_URINARY_TRACT", "SW900_LUNG", . "T24_URINARY_TRACT", "T3M10_LUNG", "T47D_BREAST", "T84_LARGE_INTESTINE", . "T98G_CENTRAL_NERVOUS_SYSTEM", "TC71_BONE", "TCCSUP_URINARY_TRACT", . "TE1_OESOPHAGUS", "TE15_OESOPHAGUS", "TE5_OESOPHAGUS", "TE617T_SOFT_TISSUE", . "TE9_OESOPHAGUS", "TEN_ENDOMETRIUM", "TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "TOV112D_OVARY", "TOV21G_OVARY", "TT2609C02_THYROID", "TYKNU_OVARY", . "U118MG_CENTRAL_NERVOUS_SYSTEM", "U2OS_BONE", "U87MG_CENTRAL_NERVOUS_SYSTEM", . "U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "UACC257_SKIN", "UACC62_SKIN", . "UACC812_BREAST", "UMUC3_URINARY_TRACT", "VMRCLCD_LUNG", "VMRCRCW_KIDNEY", . "VMRCRCZ_KIDNEY", "WM115_SKIN", "WM1799_SKIN", "WM2664_SKIN", . "WM793_SKIN", "WM88_SKIN", "WM983B_SKIN", "YKG1_CENTRAL_NERVOUS_SYSTEM", . "ZR751_BREAST", "ZR7530_BREAST", "1321N1_CENTRAL_NERVOUS_SYSTEM", . "22RV1_PROSTATE", "42MGBA_CENTRAL_NERVOUS_SYSTEM", "5637_URINARY_TRACT", . "639V_URINARY_TRACT", "697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "769P_KIDNEY", "786O_KIDNEY", "8305C_THYROID", "8505C_THYROID", . "8MGBA_CENTRAL_NERVOUS_SYSTEM", "A172_CENTRAL_NERVOUS_SYSTEM", . "A204_SOFT_TISSUE", "A2058_SKIN", "A253_SALIVARY_GLAND", "A2780_OVARY", . "A375_SKIN", "A549_LUNG", "A673_BONE", "ACHN_KIDNEY", "ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "AN3CA_ENDOMETRIUM", . "ASPC1_PANCREAS", "AU565_BREAST", "AZ521_STOMACH", "BCPAP_THYROID", . "BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BFTC909_KIDNEY", . "BGC823_STOMACH", "BL41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "BL70_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BT20_BREAST", "BT474_BREAST", . "BT549_BREAST", "BXPC3_PANCREAS", "C2BBE1_LARGE_INTESTINE", "C32_SKIN", . "C3A_LIVER", "CAKI2_KIDNEY", "CAL12T_LUNG", "CAL27_UPPER_AERODIGESTIVE_TRACT", . "CAL78_BONE", "CAL851_BREAST", "CALU1_LUNG", "CALU3_LUNG", "CALU6_LUNG", . "CAMA1_BREAST", "CAPAN2_PANCREAS", "CAS1_CENTRAL_NERVOUS_SYSTEM", . "CCK81_LARGE_INTESTINE", "CHL1_SKIN", "CHP212_AUTONOMIC_GANGLIA", . "CI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "CMK86_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "COLO201_LARGE_INTESTINE", "COLO205_LARGE_INTESTINE", "COLO320_LARGE_INTESTINE", . "COLO677_LUNG", "COLO678_LARGE_INTESTINE", "COLO679_SKIN", "COLO699_LUNG", . "COLO741_SKIN", "CORL105_LUNG", "CORL23_LUNG", "COV318_OVARY", . "COV504_OVARY", "DAOY_CENTRAL_NERVOUS_SYSTEM", "DBTRG05MG_CENTRAL_NERVOUS_SYSTEM", . "DEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DETROIT562_UPPER_AERODIGESTIVE_TRACT", . "DKMG_CENTRAL_NERVOUS_SYSTEM", "DMS114_LUNG", "DOHH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "DOV13_OVARY", "DU145_PROSTATE", "DV90_LUNG", "EB1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EB2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "EBC1_LUNG", "EFE184_ENDOMETRIUM", . "EFM19_BREAST", "EFO21_OVARY", "EFO27_OVARY", "EM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EN_ENDOMETRIUM", "ES2_OVARY", "ESS1_ENDOMETRIUM", "F36P_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "FADU_UPPER_AERODIGESTIVE_TRACT", "FU97_STOMACH", "FUOV1_OVARY", . "G361_SKIN", "G401_SOFT_TISSUE", "G402_SOFT_TISSUE", "GAMG_CENTRAL_NERVOUS_SYSTEM", . "GB1_CENTRAL_NERVOUS_SYSTEM", "GCIY_STOMACH", "GCT_SOFT_TISSUE", . "GI1_CENTRAL_NERVOUS_SYSTEM", "GLC82_LUNG", "GMS10_CENTRAL_NERVOUS_SYSTEM", . "GRANTA519_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "H4_CENTRAL_NERVOUS_SYSTEM", . "HARA_LUNG", "HCC1187_BREAST", "HCC1395_BREAST", "HCC15_LUNG", . "HCC1569_BREAST", "HCC1806_BREAST", "HCC1954_BREAST", "HCC2935_LUNG", . "HCC4006_LUNG", "HCC44_LUNG", "HCC56_LARGE_INTESTINE", "HCC70_BREAST", . "HCC78_LUNG", "HCC827_LUNG", "HCT116_LARGE_INTESTINE", "HCT15_LARGE_INTESTINE", . "HDMYZ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HDQP1_BREAST", "HEC151_ENDOMETRIUM", . "HEC1A_ENDOMETRIUM", "HEC1B_ENDOMETRIUM", "HEC251_ENDOMETRIUM", . "HEC265_ENDOMETRIUM", "HEC59_ENDOMETRIUM", "HEC6_ENDOMETRIUM", . "HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HEP3B217_LIVER", . "HEPG2_LIVER", "HEYA8_OVARY", "HGC27_STOMACH", "HH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HLE_LIVER", "HLF_LIVER", "HMC18_BREAST", "HMCB_SKIN", "HOS_BONE", . "HPAC_PANCREAS", "HPAFII_PANCREAS", "HS229T_LUNG", "HS294T_SKIN", . "HS578T_BREAST", "HS683_CENTRAL_NERVOUS_SYSTEM", "HS695T_SKIN", . "HS729_SOFT_TISSUE", "HS739T_BREAST", "HS746T_STOMACH", "HS766T_PANCREAS", . "HS840T_UPPER_AERODIGESTIVE_TRACT", "HS852T_SKIN", "HS895T_SKIN", . "HS936T_SKIN", "HS939T_SKIN", "HS944T_SKIN", "HSC2_UPPER_AERODIGESTIVE_TRACT", . "HT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HT1080_SOFT_TISSUE", . "HT1197_URINARY_TRACT", "HT1376_URINARY_TRACT", "HT144_SKIN", . "HT29_LARGE_INTESTINE", "HUCCT1_BILIARY_TRACT", "HUH1_LIVER", . "HUPT3_PANCREAS", "HUPT4_PANCREAS", "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "IALM_LUNG", "IGR37_SKIN", "IGR39_SKIN", "IGROV1_OVARY", "IM95_STOMACH", . "IMR32_AUTONOMIC_GANGLIA", "IPC298_SKIN", "ISHIKAWAHERAKLIO02ER_ENDOMETRIUM", . "ISTMES1_PLEURA", "ISTMES2_PLEURA", "J82_URINARY_TRACT", "JHH2_LIVER", . "JHH4_LIVER", "JHH5_LIVER", "JHH6_LIVER", "JHH7_LIVER", "JHOS2_OVARY", . "JHOS4_OVARY", "JHUEM2_ENDOMETRIUM", "JM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JMSU1_URINARY_TRACT", "JURKAT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JVM3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "K029AX_SKIN", "KALS1_CENTRAL_NERVOUS_SYSTEM", . "KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KARPAS620_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KCIMOH1_PANCREAS", "KCL22_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KE39_STOMACH", "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KELLY_AUTONOMIC_GANGLIA", . "KG1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KHM1B_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KLE_ENDOMETRIUM", "KMBC2_URINARY_TRACT", "KMM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMRC1_KIDNEY", "KMRC2_KIDNEY", "KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS12BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KNS42_CENTRAL_NERVOUS_SYSTEM", . "KNS60_CENTRAL_NERVOUS_SYSTEM", "KNS62_LUNG", "KNS81_CENTRAL_NERVOUS_SYSTEM", . "KO52_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KP1N_PANCREAS", "KP1NL_PANCREAS", . "KP2_PANCREAS", "KP3_PANCREAS", "KP4_PANCREAS", "KPNSI9S_AUTONOMIC_GANGLIA", . "KU812_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KURAMOCHI_OVARY", . "KYM1_SOFT_TISSUE", "KYSE140_OESOPHAGUS", "KYSE150_OESOPHAGUS", . "KYSE180_OESOPHAGUS", "KYSE30_OESOPHAGUS", "KYSE410_OESOPHAGUS", . "KYSE450_OESOPHAGUS", "KYSE510_OESOPHAGUS", "KYSE520_OESOPHAGUS", . "KYSE70_OESOPHAGUS", "L33_PANCREAS", "L363_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "L428_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LC1SQSF_LUNG", "LCLC103H_LUNG", . "LN18_CENTRAL_NERVOUS_SYSTEM", "LN229_CENTRAL_NERVOUS_SYSTEM", . "LOUNH91_LUNG", "LOXIMVI_SKIN", "LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "LS123_LARGE_INTESTINE", "LS411N_LARGE_INTESTINE", "LS513_LARGE_INTESTINE", . "LU99_LUNG", "LUDLU1_LUNG", "M059J", "MALME3M_SKIN", "MB157_BREAST", . "MC116_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MCAS_OVARY", "MCF7_BREAST", . "MDAMB157_BREAST", "MDAMB175VII_BREAST", "MDAMB415_BREAST", "MDAMB435S_SKIN", . "MDAMB436_BREAST", "MDAMB453_BREAST", "MDAMB468_BREAST", "MEC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MEG01_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MELHO_SKIN", "MESSA_SOFT_TISSUE", . "MEWO_SKIN", "MFE280_ENDOMETRIUM", "MFE296_ENDOMETRIUM", "MFE319_ENDOMETRIUM", . "MG63_BONE", "MHHES1_BONE", "MIAPACA2_PANCREAS", "MINO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MKN45_STOMACH", "MKN7_STOMACH", . "MKN74_STOMACH", "MOGGCCM_CENTRAL_NERVOUS_SYSTEM", "MOLP8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MOLT16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MONOMAC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MORCPR_LUNG", "MPP89_PLEURA", "MSTO211H_PLEURA", "NCIH1048_LUNG", . "NCIH1092_LUNG", "NCIH1155_LUNG", "NCIH1184_LUNG", "NCIH1299_LUNG", . "NCIH1339_LUNG", "NCIH1341_LUNG", "NCIH1355_LUNG", "NCIH1373_LUNG", . "NCIH1563_LUNG", "NCIH1568_LUNG", "NCIH1573_LUNG", "NCIH1581_LUNG", . "NCIH1648_LUNG", "NCIH1650_LUNG", "NCIH1651_LUNG", "NCIH1666_LUNG", . "NCIH1693_LUNG", "NCIH1694_LUNG", "NCIH1703_LUNG", "NCIH1792_LUNG", . "NCIH1793_LUNG", "NCIH1869_LUNG", "NCIH1915_LUNG", "NCIH1944_LUNG", . "NCIH1975_LUNG", "NCIH2009_LUNG", "NCIH2023_LUNG", "NCIH2030_LUNG", . "NCIH2052_PLEURA", "NCIH2085_LUNG", "NCIH2087_LUNG", "NCIH211_LUNG", . "NCIH2122_LUNG", "NCIH2170_LUNG", "NCIH2172_LUNG", "NCIH2228_LUNG", . "NCIH226_LUNG", "NCIH2286_LUNG", "NCIH23_LUNG", "NCIH2444_LUNG", . "NCIH2452_PLEURA", "NCIH28_PLEURA", "NCIH322_LUNG", "NCIH3255_LUNG", . "NCIH358_LUNG", "NCIH441_LUNG", "NCIH460_LUNG", "NCIH520_LUNG", . "NCIH522_LUNG", "NCIH647_LUNG", "NCIH650_LUNG", "NCIH661_LUNG", . "NCIH727_LUNG", "NCIH747_LARGE_INTESTINE", "NCIH810_LUNG", "NCIN87_STOMACH", . "NCO2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NIHOVCAR3_OVARY", . "NUGC3_STOMACH", "NUGC4_STOMACH", "OC314_OVARY", "OC315_OVARY", . "OC316_OVARY", "OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCIAML5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCILY10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCUM1_STOMACH", "OE21_OESOPHAGUS", "OE33_OESOPHAGUS", "ONCODG1_OVARY", . "ONS76_CENTRAL_NERVOUS_SYSTEM", "OPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OV90_OVARY", "OVCAR4_OVARY", "OVCAR8_OVARY", "OVMANA_OVARY", . "OVSAHO_OVARY", "OVTOKO_OVARY", "P12ICHIKAWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "PANC0203_PANCREAS", "PANC0327_PANCREAS", "PANC0403_PANCREAS", . "PANC1005_PANCREAS", "PATU8902_PANCREAS", "PC14_LUNG", "PC3_PROSTATE", . "PFEIFFER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PK1_PANCREAS", . "PK45H_PANCREAS", "PK59_PANCREAS", "PL45_PANCREAS", "PLCPRF5_LIVER", . "PSN1_PANCREAS", "QGP1_PANCREAS", "RAJI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RD_SOFT_TISSUE", "REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RERFGC1B_STOMACH", . "RERFLCAI_LUNG", "RERFLCMS_LUNG", "RKN_OVARY", "RKO_LARGE_INTESTINE", . "RL952_ENDOMETRIUM", "RPMI7951_SKIN", "RPMI8402_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RT112_URINARY_TRACT", "RT4_URINARY_TRACT", "RVH421_SKIN", "SAOS2_BONE", . "SBC5_LUNG", "SCABER_URINARY_TRACT", "SCC9_UPPER_AERODIGESTIVE_TRACT", . "SF126_CENTRAL_NERVOUS_SYSTEM", "SF295_CENTRAL_NERVOUS_SYSTEM", . "SF8657", "SH10TC_STOMACH", "SHP77_LUNG", "SIGM5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SIMA_AUTONOMIC_GANGLIA", "SJRH30_SOFT_TISSUE", "SJSA1_BONE", . "SKBR3_BREAST", "SKCO1_LARGE_INTESTINE", "SKES1_BONE", "SKHEP1_LIVER", . "SKLMS1_SOFT_TISSUE", "SKLU1_LUNG", "SKMEL2_SKIN", "SKMEL24_SKIN", . "SKMEL30_SKIN", "SKMEL31_SKIN", "SKMEL5_SKIN", "SKMES1_LUNG", . "SKMM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SKNAS_AUTONOMIC_GANGLIA", . "SKNBE2_AUTONOMIC_GANGLIA", "SKNDZ_AUTONOMIC_GANGLIA", "SKNFI_AUTONOMIC_GANGLIA", . "SKNSH_AUTONOMIC_GANGLIA", "SKOV3_OVARY", "SNGM_ENDOMETRIUM", . "SNU1_STOMACH", "SNU16_STOMACH", "SNU182_LIVER", "SNU387_LIVER", . "SNU398_LIVER", "SNU423_LIVER", "SNU449_LIVER", "SNU475_LIVER", . "SNUC2A_LARGE_INTESTINE", "SNUC2B", "SQ1_LUNG", "SU8686_PANCREAS", . "SUDHL10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUDHL6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUIT2_PANCREAS", "SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SW1088_CENTRAL_NERVOUS_SYSTEM", . "SW1271_LUNG", "SW1353_BONE", "SW1417_LARGE_INTESTINE", "SW1573_LUNG", . "SW1990_PANCREAS", "SW403_LARGE_INTESTINE", "SW48_LARGE_INTESTINE", . "SW480_LARGE_INTESTINE", "SW579_THYROID", "SW620_LARGE_INTESTINE", . "SW780_URINARY_TRACT", "SW900_LUNG", "T24_URINARY_TRACT", "T3M10_LUNG", . "T47D_BREAST", "T84_LARGE_INTESTINE", "T98G_CENTRAL_NERVOUS_SYSTEM", . "TC71_BONE", "TCCSUP_URINARY_TRACT", "TE1_OESOPHAGUS", "TE11_OESOPHAGUS", . "TE15_OESOPHAGUS", "TE5_OESOPHAGUS", "TE617T_SOFT_TISSUE", "TE9_OESOPHAGUS", . "TEN_ENDOMETRIUM", "TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "TOV112D_OVARY", "TOV21G_OVARY", "TT2609C02_THYROID", "TYKNU_OVARY", . "U118MG_CENTRAL_NERVOUS_SYSTEM", "U2OS_BONE", "U87MG_CENTRAL_NERVOUS_SYSTEM", . "U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "UACC257_SKIN", "UACC62_SKIN", . "UACC812_BREAST", "UMUC3_URINARY_TRACT", "VMRCLCD_LUNG", "VMRCRCW_KIDNEY", . "VMRCRCZ_KIDNEY", "WM115_SKIN", "WM1799_SKIN", "WM2664_SKIN", . "WM793_SKIN", "WM88_SKIN", "WM983B_SKIN", "YKG1_CENTRAL_NERVOUS_SYSTEM", . "ZR751_BREAST", "ZR7530_BREAST"), subscript_type = "character", . names = c("Name", "Description", "22RV1_PROSTATE", "2313287_STOMACH", . "253JBV_URINARY_TRACT", "253J_URINARY_TRACT", "42MGBA_CENTRAL_NERVOUS_SYSTEM", . "5637_URINARY_TRACT", "59M_OVARY", "639V_URINARY_TRACT", . "647V_URINARY_TRACT", "697_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "769P_KIDNEY", "786O_KIDNEY", "8305C_THYROID", "8505C_THYROID", . "8MGBA_CENTRAL_NERVOUS_SYSTEM", "A101D_SKIN", "A1207_CENTRAL_NERVOUS_SYSTEM", . "A172_CENTRAL_NERVOUS_SYSTEM", "A204_SOFT_TISSUE", "A2058_SKIN", . "A253_SALIVARY_GLAND", "A2780_OVARY", "A375_SKIN", "A3KAW_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "A427_LUNG", "A498_KIDNEY", "A4FUK_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "A549_LUNG", "A673_BONE", "A704_KIDNEY", "ABC1_LUNG", "ACCMESO1_PLEURA", . "ACHN_KIDNEY", "AGS_STOMACH", "ALLSIL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "AM38_CENTRAL_NERVOUS_SYSTEM", "AML193_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "AMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "AN3CA_ENDOMETRIUM", . "ASPC1_PANCREAS", "AU565_BREAST", "BC3C_URINARY_TRACT", "BCP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "BCPAP_THYROID", "BDCM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "BEN_LUNG", "BFTC905_URINARY_TRACT", "BFTC909_KIDNEY", "BHT101_THYROID", . "BHY_UPPER_AERODIGESTIVE_TRACT", "BICR16_UPPER_AERODIGESTIVE_TRACT", . "BICR18_UPPER_AERODIGESTIVE_TRACT", "BICR22_UPPER_AERODIGESTIVE_TRACT", . "BICR31_UPPER_AERODIGESTIVE_TRACT", "BICR56_UPPER_AERODIGESTIVE_TRACT", . "BICR6_UPPER_AERODIGESTIVE_TRACT", "BL41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "BL70_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BT12_SOFT_TISSUE", . "BT20_BREAST", "BT474_BREAST", "BT483_BREAST", "BT549_BREAST", . "BV173_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "BXPC3_PANCREAS", . "C2BBE1_LARGE_INTESTINE", "C32_SKIN", "C8166_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "CA46_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "CADOES1_BONE", . "CAKI1_KIDNEY", "CAKI2_KIDNEY", "CAL120_BREAST", "CAL12T_LUNG", . "CAL148_BREAST", "CAL27_UPPER_AERODIGESTIVE_TRACT", "CAL29_URINARY_TRACT", . "CAL33_UPPER_AERODIGESTIVE_TRACT", "CAL51_BREAST", "CAL54_KIDNEY", . "CAL62_THYROID", "CAL78_BONE", "CAL851_BREAST", "CALU1_LUNG", . "CALU3_LUNG", "CALU6_LUNG", "CAMA1_BREAST", "CAOV3_OVARY", . "CAOV4_OVARY", "CAPAN1_PANCREAS", "CAPAN2_PANCREAS", "CAS1_CENTRAL_NERVOUS_SYSTEM", . "CCFSTTG1_CENTRAL_NERVOUS_SYSTEM", "CCK81_LARGE_INTESTINE", . "CFPAC1_PANCREAS", "CH157MN_CENTRAL_NERVOUS_SYSTEM", "CHAGOK1_LUNG", . "CHP126_AUTONOMIC_GANGLIA", "CHP212_AUTONOMIC_GANGLIA", "CI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "CJM_SKIN", "CL11_LARGE_INTESTINE", "CL14_LARGE_INTESTINE", . "CL34_LARGE_INTESTINE", "CL40_LARGE_INTESTINE", "CMK_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "CMLT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "COLO201_LARGE_INTESTINE", . "COLO320_LARGE_INTESTINE", "COLO668_LUNG", "COLO678_LARGE_INTESTINE", . "COLO679_SKIN", "COLO680N_OESOPHAGUS", "COLO684_ENDOMETRIUM", . "COLO741_SKIN", "COLO783_SKIN", "COLO792_SKIN", "COLO800_SKIN", . "COLO829_SKIN", "CORL105_LUNG", "CORL23_LUNG", "CORL24_LUNG", . "CORL279_LUNG", "CORL311_LUNG", "CORL47_LUNG", "CORL88_LUNG", . "CORL95_LUNG", "COV318_OVARY", "COV362_OVARY", "COV434_OVARY", . "COV644_OVARY", "CW2_LARGE_INTESTINE", "CACO2_LARGE_INTESTINE", . "D283MED_CENTRAL_NERVOUS_SYSTEM", "D341MED_CENTRAL_NERVOUS_SYSTEM", . "DANG_PANCREAS", "DAOY_CENTRAL_NERVOUS_SYSTEM", "DAUDI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "DBTRG05MG_CENTRAL_NERVOUS_SYSTEM", "DB_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "DEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DETROIT562_UPPER_AERODIGESTIVE_TRACT", . "DKMG_CENTRAL_NERVOUS_SYSTEM", "DM3_FIBROBLAST", "DMS114_LUNG", . "DMS153_LUNG", "DMS273_LUNG", "DMS454_LUNG", "DMS53_LUNG", . "DMS79_LUNG", "DND41_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "DOHH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "DU145_PROSTATE", . "DU4475_BREAST", "DV90_LUNG", "EB1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EBC1_LUNG", "ECC10_STOMACH", "ECC12_STOMACH", "ECGI10_OESOPHAGUS", . "EFE184_ENDOMETRIUM", "EFM192A_BREAST", "EFM19_BREAST", "EFO21_OVARY", . "EFO27_OVARY", "EHEB_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EJM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "EKVX_LUNG", "EM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EN_ENDOMETRIUM", "EOL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "EPLC272H_LUNG", "ES2_OVARY", "ESS1_ENDOMETRIUM", "EW8_BONE", . "EWS502_BONE", "F36P_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "FADU_UPPER_AERODIGESTIVE_TRACT", "FTC133_THYROID", "FTC238_THYROID", . "FU97_STOMACH", "FUOV1_OVARY", "G292CLONEA141B1_BONE", "G361_SKIN", . "G401_SOFT_TISSUE", "G402_SOFT_TISSUE", "GA10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "GAMG_CENTRAL_NERVOUS_SYSTEM", "GB1_CENTRAL_NERVOUS_SYSTEM", . "GCIY_STOMACH", "GCT_SOFT_TISSUE", "GDM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "GI1_CENTRAL_NERVOUS_SYSTEM", "GMS10_CENTRAL_NERVOUS_SYSTEM", . "GOS3_CENTRAL_NERVOUS_SYSTEM", "GP2D_LARGE_INTESTINE", "GRANTA519_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "GSS_STOMACH", "GSU_STOMACH", "H4_CENTRAL_NERVOUS_SYSTEM", . "HARA_LUNG", "HCC1143_BREAST", "HCC1171_LUNG", "HCC1187_BREAST", . "HCC1195_LUNG", "HCC1359_LUNG", "HCC1395_BREAST", "HCC1419_BREAST", . "HCC1428_BREAST", "HCC1438_LUNG", "HCC1500_BREAST", "HCC1569_BREAST", . "HCC1588_LUNG", "HCC1599_BREAST", "HCC15_LUNG", "HCC1806_BREAST", . "HCC1833_LUNG", "HCC1937_BREAST", "HCC1954_BREAST", "HCC202_BREAST", . "HCC2108_LUNG", "HCC2157_BREAST", "HCC2218_BREAST", "HCC2279_LUNG", . "HCC2429_LUNG", "HCC2450_LUNG", "HCC2814_LUNG", "HCC2935_LUNG", . "HCC33_LUNG", "HCC364_LUNG", "HCC366_LUNG", "HCC38_BREAST", . "HCC4006_LUNG", "HCC44_LUNG", "HCC461_LUNG", "HCC515_LUNG", . "HCC56_LARGE_INTESTINE", "HCC70_BREAST", "HCC78_LUNG", "HCC827GR5_LUNG", . "HCC827_LUNG", "HCC95_LUNG", "HCT116_LARGE_INTESTINE", "HCT15_LARGE_INTESTINE", . "HDLM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HDMYZ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HDQP1_BREAST", "HEC108_ENDOMETRIUM", "HEC151_ENDOMETRIUM", . "HEC1A_ENDOMETRIUM", "HEC1B_ENDOMETRIUM", "HEC251_ENDOMETRIUM", . "HEC265_ENDOMETRIUM", "HEC50B_ENDOMETRIUM", "HEC59_ENDOMETRIUM", . "HEC6_ENDOMETRIUM", "HEKTE_KIDNEY", "HEL9217_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HEL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HEP3B217_LIVER", . "HEPG2_LIVER", "HEYA8_OVARY", "HGC27_STOMACH", "HH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HL60_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HLFA_FIBROBLAST", . "HLF_LIVER", "HMC18_BREAST", "HMCB_SKIN", "HMEL_BREAST", . "HNT34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HOP62_LUNG", . "HOP92_LUNG", "HOS_BONE", "HPAC_PANCREAS", "HPAFII_PANCREAS", . "HPBALL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HS172T_FIBROBLAST", . "HS229T_FIBROBLAST", "HS255T_FIBROBLAST", "HS274T_BREAST", . "HS281T_FIBROBLAST", "HS294T_SKIN", "HS343T_FIBROBLAST", . "HS578T_BREAST", "HS600T_FIBROBLAST", "HS606T_FIBROBLAST", . "HS611T_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HS616T_FIBROBLAST", . "HS618T_FIBROBLAST", "HS675T_FIBROBLAST", "HS683_CENTRAL_NERVOUS_SYSTEM", . "HS688AT_FIBROBLAST", "HS695T_SKIN", "HS698T_FIBROBLAST", . "HS706T_BONE", "HS729_SOFT_TISSUE", "HS737T_FIBROBLAST", . "HS739T_FIBROBLAST", "HS742T_FIBROBLAST", "HS746T_STOMACH", . "HS751T_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HS766T_PANCREAS", . "HS819T_FIBROBLAST", "HS821T_FIBROBLAST", "HS822T_FIBROBLAST", . "HS834T_FIBROBLAST", "HS839T_FIBROBLAST", "HS840T_FIBROBLAST", . "HS852T_SKIN", "HS863T_FIBROBLAST", "HS870T_FIBROBLAST", . "HS888T_FIBROBLAST", "HS895T_FIBROBLAST", "HS934T_FIBROBLAST", . "HS936T_SKIN", "HS939T_SKIN", "HS940T_FIBROBLAST", "HS944T_SKIN", . "HSC2_UPPER_AERODIGESTIVE_TRACT", "HSC3_UPPER_AERODIGESTIVE_TRACT", . "HSC4_UPPER_AERODIGESTIVE_TRACT", "HT1080_SOFT_TISSUE", "HT115_LARGE_INTESTINE", . "HT1197_URINARY_TRACT", "HT1376_URINARY_TRACT", "HT144_SKIN", . "HT29_LARGE_INTESTINE", "HT55_LARGE_INTESTINE", "HT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HUCCT1_BILIARY_TRACT", "HUG1N_STOMACH", "HUH1_LIVER", "HUH28_BILIARY_TRACT", . "HUH6_LIVER", "HUH7_LIVER", "HUNS1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HUPT3_PANCREAS", "HUPT4_PANCREAS", "HUT102_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "HUTU80_SMALL_INTESTINE", . "HELA_CERVIX", "IALM_LUNG", "IGR1_SKIN", "IGR37_SKIN", "IGR39_SKIN", . "IGROV1_OVARY", "IM95_STOMACH", "IMR32_AUTONOMIC_GANGLIA", . "INA6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "IOMMLEE_CENTRAL_NERVOUS_SYSTEM", . "IPC298_SKIN", "ISHIKAWAHERAKLIO02ER_ENDOMETRIUM", "ISTMES1_PLEURA", . "ISTMES2_PLEURA", "J82_URINARY_TRACT", "JEKO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JHESOAD1_OESOPHAGUS", "JHH1_LIVER", "JHH2_LIVER", "JHH4_LIVER", . "JHH5_LIVER", "JHH6_LIVER", "JHH7_LIVER", "JHOC5_OVARY", . "JHOM1_OVARY", "JHOM2B_OVARY", "JHOS2_OVARY", "JHOS4_OVARY", . "JHUEM1_ENDOMETRIUM", "JHUEM2_ENDOMETRIUM", "JHUEM3_ENDOMETRIUM", . "JHUEM7_ENDOMETRIUM", "JIMT1_BREAST", "JJN3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JK1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "JL1_PLEURA", "JM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JMSU1_URINARY_TRACT", "JURKAT_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JURLMK1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "JVM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "JVM3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "K029AX_SKIN", . "K562_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KALS1_CENTRAL_NERVOUS_SYSTEM", . "KARPAS299_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KARPAS422_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KARPAS620_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KASUMI2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KASUMI6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KATOIII_STOMACH", "KCL22_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KD_SOFT_TISSUE", "KE37_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KE39_STOMACH", "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KELLY_AUTONOMIC_GANGLIA", "KG1C_CENTRAL_NERVOUS_SYSTEM", . "KG1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KHM1B_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KIJK_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KLE_ENDOMETRIUM", . "KM12_LARGE_INTESTINE", "KMBC2_URINARY_TRACT", "KMH2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMRC1_KIDNEY", . "KMRC20_KIDNEY", "KMRC2_KIDNEY", "KMRC3_KIDNEY", "KMS18_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS12BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS20_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS21BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS26_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS27_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KMS28BM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KMS34_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KNS42_CENTRAL_NERVOUS_SYSTEM", "KNS60_CENTRAL_NERVOUS_SYSTEM", . "KNS62_LUNG", "KNS81_CENTRAL_NERVOUS_SYSTEM", "KO52_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KOPN8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "KP2_PANCREAS", . "KP3_PANCREAS", "KP4_PANCREAS", "KPL1_BREAST", "KPMRTRY_SOFT_TISSUE", . "KPNRTBM1_AUTONOMIC_GANGLIA", "KPNSI9S_AUTONOMIC_GANGLIA", . "KPNYN_AUTONOMIC_GANGLIA", "KS1_CENTRAL_NERVOUS_SYSTEM", . "KU1919_URINARY_TRACT", "KU812_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KURAMOCHI_OVARY", "KYM1_SOFT_TISSUE", "KYO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "KYSE140_OESOPHAGUS", "KYSE150_OESOPHAGUS", "KYSE180_OESOPHAGUS", . "KYSE270_OESOPHAGUS", "KYSE30_OESOPHAGUS", "KYSE410_OESOPHAGUS", . "KYSE450_OESOPHAGUS", "KYSE510_OESOPHAGUS", "KYSE520_OESOPHAGUS", . "KYSE70_OESOPHAGUS", "L1236_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "L33_PANCREAS", "L363_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "L428_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "L540_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "LAMA84_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LC1F_LUNG", . "LCLC103H_LUNG", "LCLC97TM1_LUNG", "LI7_LIVER", "LK2_LUNG", . "LMSU_STOMACH", "LN18_CENTRAL_NERVOUS_SYSTEM", "LN215_CENTRAL_NERVOUS_SYSTEM", . "LN229_CENTRAL_NERVOUS_SYSTEM", "LN235_CENTRAL_NERVOUS_SYSTEM", . "LN319_CENTRAL_NERVOUS_SYSTEM", "LN340_CENTRAL_NERVOUS_SYSTEM", . "LN382_CENTRAL_NERVOUS_SYSTEM", "LN428_CENTRAL_NERVOUS_SYSTEM", . "LN443_CENTRAL_NERVOUS_SYSTEM", "LN464_CENTRAL_NERVOUS_SYSTEM", . "LNCAPCLONEFGC_PROSTATE", "LNZ308_CENTRAL_NERVOUS_SYSTEM", . "LOUCY_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "LOUNH91_LUNG", . "LOVO_LARGE_INTESTINE", "LOXIMVI_SKIN", "LP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "LS1034_LARGE_INTESTINE", "LS123_LARGE_INTESTINE", "LS180_LARGE_INTESTINE", . "LS411N_LARGE_INTESTINE", "LS513_LARGE_INTESTINE", "LU65_LUNG", . "LU99_LUNG", "LUDLU1_LUNG", "LXF289_LUNG", "M059K_CENTRAL_NERVOUS_SYSTEM", . "M07E_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MALME3M_SKIN", . "MC116_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MCAS_OVARY", . "MCF7_BREAST", "MDAMB134VI_BREAST", "MDAMB157_BREAST", "MDAMB175VII_BREAST", . "MDAMB231_BREAST", "MDAMB361_BREAST", "MDAMB415_BREAST", . "MDAMB435S_SKIN", "MDAMB436_BREAST", "MDAMB453_BREAST", "MDAMB468_BREAST", . "MDAPCA2B_PROSTATE", "MDST8_LARGE_INTESTINE", "ME1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MEC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MEG01_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MELHO_SKIN", "MELJUSO_SKIN", "MESSA_SOFT_TISSUE", "MEWO_SKIN", . "MFE280_ENDOMETRIUM", "MFE296_ENDOMETRIUM", "MFE319_ENDOMETRIUM", . "MG63_BONE", "MHHCALL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MHHCALL3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MHHCALL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MHHES1_BONE", "MHHNB11_AUTONOMIC_GANGLIA", "MIAPACA2_PANCREAS", . "MINO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MKN1_STOMACH", "MKN45_STOMACH", "MKN74_STOMACH", "MKN7_STOMACH", . "ML1_THYROID", "MM1S_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MOLM13_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MOLM16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MOLM6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MOLP2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MOLP8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MOLT13_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MOLT16_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MOLT3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MON_SOFT_TISSUE", "MONOMAC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MONOMAC6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MORCPR_LUNG", . "MPP89_PLEURA", "MSTO211H_PLEURA", "MUTZ3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MUTZ5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MV411_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "NALM19_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NALM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "NALM6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NAMALWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "NB1_AUTONOMIC_GANGLIA", "NB4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "NCCSTCK140_STOMACH", "NCIH2004RT_SOFT_TISSUE", "NCIH2077_LUNG", . "NCIH2882_LUNG", "NCIH2887_LUNG", "NCIH1048_LUNG", "NCIH1092_LUNG", . "NCIH1105_LUNG", "NCIH1155_LUNG", "NCIH1184_LUNG", "NCIH1299_LUNG", . "NCIH1339_LUNG", "NCIH1341_LUNG", "NCIH1355_LUNG", "NCIH1373_LUNG", . "NCIH1385_LUNG", "NCIH1395_LUNG", "NCIH1435_LUNG", "NCIH1436_LUNG", . "NCIH1437_LUNG", "NCIH146_LUNG", "NCIH1563_LUNG", "NCIH1568_LUNG", . "NCIH1573_LUNG", "NCIH1581_LUNG", "NCIH1618_LUNG", "NCIH1623_LUNG", . "NCIH1648_LUNG", "NCIH1650_LUNG", "NCIH1651_LUNG", "NCIH1666_LUNG", . "NCIH1693_LUNG", "NCIH1694_LUNG", "NCIH1703_LUNG", "NCIH1734_LUNG", . "NCIH1755_LUNG", "NCIH1781_LUNG", "NCIH1792_LUNG", "NCIH1793_LUNG", . "NCIH1819_LUNG", "NCIH1836_LUNG", "NCIH1838_LUNG", "NCIH1869_LUNG", . "NCIH1876_LUNG", "NCIH1915_LUNG", "NCIH1930_LUNG", "NCIH1944_LUNG", . "NCIH1963_LUNG", "NCIH196_LUNG", "NCIH1975_LUNG", "NCIH2009_LUNG", . "NCIH2023_LUNG", "NCIH2029_LUNG", "NCIH2030_LUNG", "NCIH2052_PLEURA", . "NCIH2066_LUNG", "NCIH2073_LUNG", "NCIH2081_LUNG", "NCIH2085_LUNG", . "NCIH2087_LUNG", "NCIH209_LUNG", "NCIH2106_LUNG", "NCIH2110_LUNG", . "NCIH211_LUNG", "NCIH2122_LUNG", "NCIH2126_LUNG", "NCIH2170_LUNG", . "NCIH2171_LUNG", "NCIH2172_LUNG", "NCIH2196_LUNG", "NCIH2227_LUNG", . "NCIH2228_LUNG", "NCIH226_LUNG", "NCIH2286_LUNG", "NCIH2291_LUNG", . "NCIH2342_LUNG", "NCIH2347_LUNG", "NCIH23_LUNG", "NCIH2405_LUNG", . "NCIH2444_LUNG", "NCIH2452_PLEURA", "NCIH28_PLEURA", "NCIH292_LUNG", . "NCIH3122_LUNG", "NCIH322_LUNG", "NCIH3255_LUNG", "NCIH358_LUNG", . "NCIH441_LUNG", "NCIH446_LUNG", "NCIH460_LUNG", "NCIH508_LARGE_INTESTINE", . "NCIH510_LUNG", "NCIH520_LUNG", "NCIH522_LUNG", "NCIH524_LUNG", . "NCIH526_LUNG", "NCIH596_LUNG", "NCIH647_LUNG", "NCIH650_LUNG", . "NCIH660_PROSTATE", "NCIH661_LUNG", "NCIH684_LIVER", "NCIH69_LUNG", . "NCIH716_LARGE_INTESTINE", "NCIH727_LUNG", "NCIH747_LARGE_INTESTINE", . "NCIH810_LUNG", "NCIH82_LUNG", "NCIH838_LUNG", "NCIH841_LUNG", . "NCIH854_LUNG", "NCIH889_LUNG", "NCIH929_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "NCIN87_STOMACH", "NCO2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "NH6_AUTONOMIC_GANGLIA", "NHAHTDD_CENTRAL_NERVOUS_SYSTEM", . "NIHOVCAR3_OVARY", "NMCG1_CENTRAL_NERVOUS_SYSTEM", "NOMO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "NUDHL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "NUDUL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "NUGC2_STOMACH", "NUGC3_STOMACH", "NUGC4_STOMACH", "OAW28_OVARY", . "OAW42_OVARY", "OC314_OVARY", "OCIMY5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCIMY7_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCIAML2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCIAML3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCIAML5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCILY19_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCILY3_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OCIM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OCUM1_STOMACH", . "OE19_OESOPHAGUS", "OE21_OESOPHAGUS", "OE33_OESOPHAGUS", . "OELE_OVARY", "ONCODG1_OVARY", "ONS76_CENTRAL_NERVOUS_SYSTEM", . "OPM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "OPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "OSRC2_KIDNEY", "OUMS23_LARGE_INTESTINE", "OV56_OVARY", "OV7_OVARY", . "OV90_OVARY", "OVCAR4_OVARY", "OVCAR8_OVARY", "OVISE_OVARY", . "OVK18_OVARY", "OVKATE_OVARY", "OVMANA_OVARY", "OVSAHO_OVARY", . "OVTOKO_OVARY", "P12ICHIKAWA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "P31FUJ_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "PANC0203_PANCREAS", "PANC0213_PANCREAS", "PANC0327_PANCREAS", . "PANC0403_PANCREAS", "PANC0504_PANCREAS", "PANC0813_PANCREAS", . "PANC1005_PANCREAS", "PANC1_PANCREAS", "PATU8902_PANCREAS", . "PATU8988S_PANCREAS", "PATU8988T_PANCREAS", "PC9_LUNG", "PC14_LUNG", . "PC3_PROSTATE", "PECAPJ15_UPPER_AERODIGESTIVE_TRACT", "PECAPJ34CLONEC12_UPPER_AERODIGESTIVE_TRACT", . "PECAPJ41CLONED2_UPPER_AERODIGESTIVE_TRACT", "PECAPJ49_UPPER_AERODIGESTIVE_TRACT", . "PEER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PF382_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "PFEIFFER_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PK1_PANCREAS", . "PK45H_PANCREAS", "PK59_PANCREAS", "PL21_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "PLB985_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "PLCPRF5_LIVER", . "PRECLH_PROSTATE", "PSN1_PANCREAS", "QGP1_PANCREAS", "RAJI_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RCC10RGB_KIDNEY", "RCHACV_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RCM1_LARGE_INTESTINE", "RDES_BONE", "RD_SOFT_TISSUE", "REC1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "REH_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RERFGC1B_STOMACH", . "RERFLCAD1_LUNG", "RERFLCAD2_LUNG", "RERFLCAI_LUNG", "RERFLCKJ_LUNG", . "RERFLCMS_LUNG", "RERFLCSQ1_LUNG", "RH18_SOFT_TISSUE", "RH30_SOFT_TISSUE", . "RH41_SOFT_TISSUE", "RI1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RKN_SOFT_TISSUE", "RKO_LARGE_INTESTINE", "RL952_ENDOMETRIUM", . "RL_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RMGI_OVARY", "RMUGS_OVARY", . "RPMI7951_SKIN", "RPMI8226_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RPMI8402_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "RS411_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "RS5_FIBROBLAST", "RT112_URINARY_TRACT", "RT4_URINARY_TRACT", . "RVH421_SKIN", "S117_SOFT_TISSUE", "SALE_LUNG", "SAOS2_BONE", . "SBC5_LUNG", "SCABER_URINARY_TRACT", "SCC15_UPPER_AERODIGESTIVE_TRACT", . "SCC25_UPPER_AERODIGESTIVE_TRACT", "SCC4_UPPER_AERODIGESTIVE_TRACT", . "SCC9_UPPER_AERODIGESTIVE_TRACT", "SCLC21H_LUNG", "SEM_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SET2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SF126_CENTRAL_NERVOUS_SYSTEM", . "SF172_CENTRAL_NERVOUS_SYSTEM", "SF268_CENTRAL_NERVOUS_SYSTEM", . "SF295_CENTRAL_NERVOUS_SYSTEM", "SF539_CENTRAL_NERVOUS_SYSTEM", . "SF767_CENTRAL_NERVOUS_SYSTEM", "SH10TC_STOMACH", "SH4_SKIN", . "SHP77_LUNG", "SIGM5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SIMA_AUTONOMIC_GANGLIA", "SJSA1_BONE", "SKBR3_BREAST", "SKCO1_LARGE_INTESTINE", . "SKES1_BONE", "SKHEP1_LIVER", "SKLMS1_SOFT_TISSUE", "SKLU1_LUNG", . "SKM1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SKMEL1_SKIN", . "SKMEL24_SKIN", "SKMEL28_SKIN", "SKMEL30_SKIN", "SKMEL31_SKIN", . "SKMEL3_SKIN", "SKMEL5_SKIN", "SKMES1_LUNG", "SKMM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SKNAS_AUTONOMIC_GANGLIA", "SKNBE2_AUTONOMIC_GANGLIA", "SKNDZ_AUTONOMIC_GANGLIA", . "SKNEP1_BONE", "SKNFI_AUTONOMIC_GANGLIA", "SKNMC_BONE", "SKNO1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SKNSH_AUTONOMIC_GANGLIA", "SKOV3_OVARY", "SKPNDW_BONE", . "SKRC20_KIDNEY", "SKUT1_SOFT_TISSUE", "SLR20_KIDNEY", "SLR21_KIDNEY", . "SLR23_KIDNEY", "SLR24_KIDNEY", "SLR25_KIDNEY", "SLR26_KIDNEY", . "SNB75_CENTRAL_NERVOUS_SYSTEM", "SNGM_ENDOMETRIUM", "SNU1033_LARGE_INTESTINE", . "SNU1040_LARGE_INTESTINE", "SNU1041_UPPER_AERODIGESTIVE_TRACT", . "SNU1066_UPPER_AERODIGESTIVE_TRACT", "SNU1076_UPPER_AERODIGESTIVE_TRACT", . "SNU1077_ENDOMETRIUM", "SNU1079_BILIARY_TRACT", "SNU1105_CENTRAL_NERVOUS_SYSTEM", . "SNU1196_BILIARY_TRACT", "SNU1197_LARGE_INTESTINE", "SNU119_OVARY", . "SNU1214_UPPER_AERODIGESTIVE_TRACT", "SNU1272_KIDNEY", "SNU16_STOMACH", . "SNU175_LARGE_INTESTINE", "SNU182_LIVER", "SNU1_STOMACH", . "SNU201_CENTRAL_NERVOUS_SYSTEM", "SNU213_PANCREAS", "SNU216_STOMACH", . "SNU245_BILIARY_TRACT", "SNU283_LARGE_INTESTINE", "SNU308_BILIARY_TRACT", . "SNU324_PANCREAS", "SNU349_KIDNEY", "SNU387_LIVER", "SNU398_LIVER", . "SNU407_LARGE_INTESTINE", "SNU410_PANCREAS", "SNU423_LIVER", . "SNU449_LIVER", "SNU466_CENTRAL_NERVOUS_SYSTEM", "SNU46_UPPER_AERODIGESTIVE_TRACT", . "SNU475_LIVER", "SNU478_BILIARY_TRACT", "SNU489_CENTRAL_NERVOUS_SYSTEM", . "SNU503_LARGE_INTESTINE", "SNU520_STOMACH", "SNU5_STOMACH", . "SNU601_STOMACH", "SNU61_LARGE_INTESTINE", "SNU620_STOMACH", . "SNU626_CENTRAL_NERVOUS_SYSTEM", "SNU668_STOMACH", "SNU685_ENDOMETRIUM", . "SNU719_STOMACH", "SNU738_CENTRAL_NERVOUS_SYSTEM", "SNU761_LIVER", . "SNU81_LARGE_INTESTINE", "SNU840_OVARY", "SNU869_BILIARY_TRACT", . "SNU878_LIVER", "SNU886_LIVER", "SNU899_UPPER_AERODIGESTIVE_TRACT", . "SNU8_OVARY", "SNUC1_LARGE_INTESTINE", "SNUC2A_LARGE_INTESTINE", . "SNUC4_LARGE_INTESTINE", "SNUC5_LARGE_INTESTINE", "SQ1_LUNG", . "SR786_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "ST486_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "STM9101_SOFT_TISSUE", "SU8686_PANCREAS", "SUDHL10_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUDHL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL4_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUDHL5_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUDHL6_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUDHL8_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUIT2_PANCREAS", . "SUPB15_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUPM2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SUPT11_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "SUPT1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SW1088_CENTRAL_NERVOUS_SYSTEM", "SW1116_LARGE_INTESTINE", . "SW1271_LUNG", "SW1353_BONE", "SW1417_LARGE_INTESTINE", "SW1463_LARGE_INTESTINE", . "SW1573_LUNG", "SW1710_URINARY_TRACT", "SW1783_CENTRAL_NERVOUS_SYSTEM", . "SW1990_PANCREAS", "SW403_LARGE_INTESTINE", "SW480_LARGE_INTESTINE", . "SW48_LARGE_INTESTINE", "SW579_THYROID", "SW620_LARGE_INTESTINE", . "SW780_URINARY_TRACT", "SW837_LARGE_INTESTINE", "SW900_LUNG", . "SW948_LARGE_INTESTINE", "SIHA_CERVIX", "T173_FIBROBLAST", . "T24_URINARY_TRACT", "T3M10_LUNG", "T3M4_PANCREAS", "T47D_BREAST", . "T84_LARGE_INTESTINE", "T98G_CENTRAL_NERVOUS_SYSTEM", "TALL1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "TC71_BONE", "TCCPAN2_PANCREAS", "TCCSUP_URINARY_TRACT", . "TE10_OESOPHAGUS", "TE11_OESOPHAGUS", "TE125T_FIBROBLAST", . "TE14_OESOPHAGUS", "TE159T_FIBROBLAST", "TE15_OESOPHAGUS", . "TE1_OESOPHAGUS", "TE441T_SOFT_TISSUE", "TE4_OESOPHAGUS", . "TE5_OESOPHAGUS", "TE617T_SOFT_TISSUE", "TE6_OESOPHAGUS", . "TE8_OESOPHAGUS", "TE9_OESOPHAGUS", "TEN_ENDOMETRIUM", "TF1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "TGBC11TKB_STOMACH", "THP1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "TIG3TD_FIBROBLAST", "TM87_SOFT_TISSUE", "TM31_CENTRAL_NERVOUS_SYSTEM", . "TO175T_FIBROBLAST", "TOLEDO_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "TOV112D_OVARY", "TOV21G_OVARY", "TT2609C02_THYROID", "TTC709_SOFT_TISSUE", . "TTC1240_SOFT_TISSUE", "TTC549_SOFT_TISSUE", "TTC642_SOFT_TISSUE", . "TT_OESOPHAGUS", "TT_THYROID", "TUHR10TKB_KIDNEY", "TUHR14TKB_KIDNEY", . "TUHR4TKB_KIDNEY", "TYKNU_OVARY", "U118MG_CENTRAL_NERVOUS_SYSTEM", . "U178_CENTRAL_NERVOUS_SYSTEM", "U251MG_CENTRAL_NERVOUS_SYSTEM", . "U266B1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "U2OS_BONE", . "U343_CENTRAL_NERVOUS_SYSTEM", "U87MG_CENTRAL_NERVOUS_SYSTEM", . "U937_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "UACC257_SKIN", . "UACC62_SKIN", "UACC812_BREAST", "UACC893_BREAST", "UBLC1_URINARY_TRACT", . "UMUC1_URINARY_TRACT", "UMUC3_URINARY_TRACT", "UO31_KIDNEY", . "UOK101_KIDNEY", "VCAP_PROSTATE", "VMCUB1_URINARY_TRACT", . "VMRCRCW_KIDNEY", "VMRCRCZ_KIDNEY", "WM115_SKIN", "WM1799_SKIN", . "WM2664_SKIN", "WM793_SKIN", "WM88_SKIN", "WM983B_SKIN", . "WSUDLCL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "YAPC_PANCREAS", . "YD10B_UPPER_AERODIGESTIVE_TRACT", "YD15_SALIVARY_GLAND", . "YD38_UPPER_AERODIGESTIVE_TRACT", "YD8_UPPER_AERODIGESTIVE_TRACT", . "YH13_CENTRAL_NERVOUS_SYSTEM", "YKG1_CENTRAL_NERVOUS_SYSTEM", . "ZR751_BREAST", "ZR7530_BREAST", "143B_BONE", "BIN67_OVARY", . "CHLA15_AUTONOMIC_GANGLIA", "CHLA266_SOFT_TISSUE", "COGAR359_SOFT_TISSUE", . "COGE352_BONE", "COGN278_AUTONOMIC_GANGLIA", "COGN305_AUTONOMIC_GANGLIA", . "COV504_OVARY", "CW9019_SOFT_TISSUE", "D425_CENTRAL_NERVOUS_SYSTEM", . "D458_CENTRAL_NERVOUS_SYSTEM", "DLD1_LARGE_INTESTINE", "F5_CENTRAL_NERVOUS_SYSTEM", . "JR_SOFT_TISSUE", "L82_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "MAC2A_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", "MYLA_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "PEDS005TPFAD_KIDNEY", "PEDS015T_SOFT_TISSUE", "RT11284_URINARY_TRACT", . "SMSCTR_SOFT_TISSUE", "SMZ1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE", . "SW982_SOFT_TISSUE", "SYO1_SOFT_TISSUE", "TC138_BONE", "TC205_BONE", . "UPCISCC152_UPPER_AERODIGESTIVE_TRACT", "UW228_CENTRAL_NERVOUS_SYSTEM" . ), subscript_action = NULL, subscript_arg = "select_cl", . rlang = list(inherit = TRUE), call = dplyr::select(., c("Description", . select_cl))), class = c("vctrs_error_subscript_oob", . "vctrs_error_subscript", "rlang_error", "error", "condition"))) 28. handlers[[1L]](cnd) 29. cnd_signal(cnd) 30. signal_abort(cnd)
In [261]:
CCLE_exp
| Name | Description | 22RV1_PROSTATE | 2313287_STOMACH | 253JBV_URINARY_TRACT | 253J_URINARY_TRACT | 42MGBA_CENTRAL_NERVOUS_SYSTEM | 5637_URINARY_TRACT | 59M_OVARY | 639V_URINARY_TRACT | ⋯ | PEDS015T_SOFT_TISSUE | RT11284_URINARY_TRACT | SMSCTR_SOFT_TISSUE | SMZ1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE | SW982_SOFT_TISSUE | SYO1_SOFT_TISSUE | TC138_BONE | TC205_BONE | UPCISCC152_UPPER_AERODIGESTIVE_TRACT | UW228_CENTRAL_NERVOUS_SYSTEM |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| ENSG00000223972.4 | DDX11L1 | 0.00000 | 0.03755 | 0.00000 | 0.06507 | 0.00000 | 0.01378 | 0.01463 | 0.03085 | ⋯ | 0.00000 | 0.05286 | 0.02024 | 0.02800 | 0.00000 | 0.03535 | 0.00000 | 0.00000 | 0.00000 | 0.03799 |
| ENSG00000227232.4 | WASH7P | 12.63011 | 10.14155 | 6.20657 | 6.55835 | 7.61752 | 6.19356 | 4.62277 | 6.00767 | ⋯ | 6.13757 | 6.73907 | 11.10122 | 8.39780 | 5.06211 | 7.12056 | 10.75343 | 6.56657 | 15.23348 | 6.47732 |
| ENSG00000243485.2 | MIR1302-11 | 0.04289 | 0.01037 | 0.16955 | 0.23364 | 0.08765 | 0.00000 | 0.00808 | 0.09373 | ⋯ | 0.05747 | 0.16061 | 0.07825 | 0.07425 | 0.14717 | 0.03906 | 0.24068 | 0.29759 | 0.07448 | 0.01049 |
| ENSG00000237613.2 | FAM138A | 0.00000 | 0.00869 | 0.22423 | 0.16558 | 0.00000 | 0.00637 | 0.00000 | 0.01427 | ⋯ | 0.00000 | 0.00000 | 0.07491 | 0.01555 | 0.01298 | 0.00000 | 0.04480 | 0.01039 | 0.02674 | 0.00879 |
| ENSG00000268020.2 | OR4G4P | 0.00000 | 0.00000 | 0.02886 | 0.03875 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.01205 | 0.00000 | 0.00000 | 0.00000 | 0.01442 | 0.05347 | 0.00000 | 0.00000 |
| ENSG00000240361.1 | OR4G11P | 0.00000 | 0.00000 | 0.01939 | 0.03904 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00336 | 0.00000 | 0.00000 | 0.01452 | 0.01347 | 0.00000 | 0.02279 |
| ENSG00000186092.4 | OR4F5 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00344 | 0.00000 | 0.00000 | 0.01487 | 0.02758 | 0.00000 | 0.00000 |
| ENSG00000238009.2 | RP11-34P13.7 | 0.44714 | 0.34327 | 0.16982 | 0.08922 | 0.09428 | 0.10706 | 0.03342 | 0.04230 | ⋯ | 0.02377 | 0.16913 | 0.11099 | 0.40697 | 0.04486 | 0.04848 | 0.09957 | 0.07182 | 0.04402 | 0.06945 |
| ENSG00000233750.3 | CICP27 | 0.37909 | 0.09168 | 0.10995 | 0.09146 | 0.07513 | 0.06523 | 0.06276 | 0.13009 | ⋯ | 0.02309 | 0.21900 | 0.17666 | 0.12014 | 0.03734 | 0.08369 | 0.08595 | 0.10628 | 0.08265 | 0.01686 |
| ENSG00000237683.5 | AL627309.1 | 6.17780 | 6.56648 | 1.03405 | 0.76888 | 1.01231 | 4.03005 | 3.29219 | 5.23439 | ⋯ | 0.62840 | 5.91599 | 0.90505 | 1.95612 | 0.47254 | 1.12027 | 0.29244 | 0.25216 | 1.91472 | 0.21741 |
| ENSG00000268903.1 | RP11-34P13.15 | 5.52172 | 5.79291 | 0.96543 | 0.91141 | 0.93642 | 3.20103 | 3.01556 | 2.80014 | ⋯ | 0.93254 | 1.30318 | 0.77101 | 0.70282 | 0.30376 | 0.30372 | 0.01808 | 0.26829 | 1.29501 | 0.02838 |
| ENSG00000269981.1 | RP11-34P13.16 | 4.10155 | 5.14582 | 0.28874 | 0.29075 | 0.28361 | 2.95516 | 2.26559 | 4.87078 | ⋯ | 0.20659 | 6.24651 | 1.24589 | 2.10197 | 0.94677 | 1.50955 | 0.48071 | 0.13373 | 2.18038 | 0.49040 |
| ENSG00000239906.1 | RP11-34P13.14 | 2.16921 | 3.73763 | 1.24117 | 1.22142 | 1.80095 | 2.04499 | 2.22189 | 3.98638 | ⋯ | 0.09082 | 4.43075 | 0.14135 | 1.04632 | 0.04897 | 0.46300 | 0.04227 | 0.11759 | 0.73994 | 0.00000 |
| ENSG00000241860.2 | RP11-34P13.13 | 1.45051 | 1.70260 | 0.51623 | 0.47688 | 0.30626 | 0.28475 | 0.36352 | 0.94654 | ⋯ | 0.02052 | 0.84705 | 0.11792 | 0.13970 | 0.04979 | 0.21083 | 0.12121 | 0.27384 | 0.20167 | 0.07782 |
| ENSG00000222623.1 | RNU6-1100P | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000241599.1 | RP11-34P13.9 | 0.03833 | 0.04635 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.09786 | 0.00000 | 0.00691 | 0.00000 | 0.02182 | 0.00000 | 0.02770 | 0.00000 | 0.00000 |
| ENSG00000228463.4 | AP006222.2 | 1.39546 | 0.81420 | 1.49090 | 1.67532 | 1.12489 | 0.78417 | 0.31503 | 0.70630 | ⋯ | 0.32892 | 0.64095 | 0.12701 | 1.67112 | 0.23401 | 0.28241 | 1.20156 | 1.14947 | 0.18683 | 0.66111 |
| ENSG00000241670.2 | AP006222.1 | 3.42004 | 1.26904 | 4.06619 | 4.54950 | 3.18034 | 1.54135 | 1.29531 | 2.44463 | ⋯ | 1.01020 | 1.04723 | 0.30657 | 4.33317 | 0.78430 | 0.96816 | 1.83345 | 1.51713 | 0.17956 | 2.14708 |
| ENSG00000237094.7 | RP4-669L17.10 | 5.40033 | 5.15221 | 1.80699 | 1.60018 | 2.11839 | 0.97418 | 2.47912 | 1.61543 | ⋯ | 0.77455 | 0.81067 | 1.59256 | 0.55472 | 0.73843 | 0.42826 | 0.40135 | 0.45318 | 0.68839 | 0.17951 |
| ENSG00000250575.1 | RP4-669L17.8 | 5.38639 | 5.68387 | 1.14718 | 0.94784 | 1.24236 | 1.29203 | 2.75635 | 1.35521 | ⋯ | 1.11283 | 1.45587 | 3.63882 | 0.88714 | 1.54464 | 0.66789 | 0.56195 | 0.72548 | 1.85007 | 0.46692 |
| ENSG00000233653.3 | CICP7 | 0.55159 | 0.20949 | 0.15817 | 0.10742 | 0.11200 | 0.06275 | 0.09325 | 0.27045 | ⋯ | 0.06316 | 0.03611 | 0.05069 | 0.01913 | 0.00319 | 0.01208 | 0.05512 | 0.01533 | 0.00877 | 0.00000 |
| ENSG00000224813.2 | RP4-669L17.4 | 2.57880 | 0.11767 | 0.25309 | 0.15291 | 0.19887 | 0.43172 | 0.32080 | 0.29000 | ⋯ | 0.05433 | 0.08282 | 0.00000 | 0.03509 | 0.04394 | 0.00000 | 0.07585 | 0.14067 | 0.00000 | 0.00000 |
| ENSG00000235249.1 | OR4F29 | 0.05281 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00781 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.01498 | 0.00000 | 0.01270 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000269732.1 | WBP1LP7 | 0.19302 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.02747 | 0.00000 | 0.00000 | 0.02993 | 0.02951 |
| ENSG00000256186.1 | AL732372.1 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000236743.1 | RP5-857K21.15 | 0.20729 | 0.00000 | 0.00000 | 0.02714 | 0.01324 | 0.31038 | 0.00000 | 0.02574 | ⋯ | 0.01447 | 0.02205 | 0.01688 | 0.14017 | 0.02340 | 0.00000 | 0.04039 | 0.03746 | 0.00000 | 0.00000 |
| ENSG00000236601.1 | RP4-669L17.2 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000236679.2 | RP4-669L17.1 | 0.88719 | 0.24756 | 0.35499 | 0.47661 | 0.32543 | 0.18166 | 0.29997 | 0.22597 | ⋯ | 0.22859 | 0.11616 | 0.02965 | 0.04102 | 0.06162 | 0.05179 | 0.07092 | 0.29595 | 0.05643 | 0.05565 |
| ENSG00000231709.1 | RP5-857K21.1 | 0.52008 | 0.07546 | 0.06493 | 0.03269 | 0.12754 | 0.47992 | 0.03919 | 0.11366 | ⋯ | 0.01161 | 0.03541 | 0.04067 | 0.21007 | 0.00939 | 0.05920 | 0.45399 | 0.04511 | 0.02580 | 0.00000 |
| ENSG00000235146.2 | RP5-857K21.2 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.11301 | 0.03127 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| ENSGR0000270726.1 | AJ271736.10 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSGR0000185203.7 | WASIR1 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSGR0000227159.3 | DDX11L16 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000210049.1 | MT-TF | 0.00000 | 0.00000 | 0.00000 | 0.25845 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 1.04983 | 0.16076 | 0.00000 | 0.00000 | 0.28085 | 0.38457 | 0.17831 | 0.00000 | 0.45267 |
| ENSG00000211459.2 | MT-RNR1 | 910.27307 | 469.22092 | 1786.56653 | 2176.14771 | 884.79218 | 301.08878 | 2614.87988 | 675.11743 | ⋯ | 1555.49182 | 1269.64868 | 377.95169 | 395.04962 | 452.11700 | 973.65546 | 981.28265 | 1460.00427 | 555.72089 | 291.52655 |
| ENSG00000210077.1 | MT-TV | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.14172 | 0.21605 | 0.00000 | 0.04578 | 0.00000 | 0.28899 | 0.59357 | 0.36696 | 0.00000 | 0.00000 |
| ENSG00000210082.2 | MT-RNR2 | 5638.01953 | 1781.06616 | 7849.11230 | 16147.12598 | 2471.76538 | 1613.67700 | 8680.26855 | 1838.13623 | ⋯ | 5183.72119 | 3302.51294 | 1707.74731 | 1253.85999 | 855.83307 | 10336.67676 | 3690.90479 | 7264.39111 | 2183.91016 | 1826.41577 |
| ENSG00000209082.1 | MT-TL1 | 1.98519 | 0.14120 | 0.24297 | 0.00000 | 0.11932 | 0.82890 | 0.65993 | 0.23200 | ⋯ | 0.78230 | 0.19877 | 0.60874 | 0.29479 | 0.10544 | 0.26587 | 0.54609 | 1.85682 | 0.00000 | 0.42853 |
| ENSG00000198888.2 | MT-ND1 | 2925.43628 | 1367.65308 | 1888.47595 | 2255.65625 | 984.25763 | 1918.51343 | 2427.98145 | 2112.54736 | ⋯ | 2769.03271 | 2440.45386 | 1518.37463 | 901.30054 | 381.61768 | 1091.19629 | 1445.38892 | 3173.04883 | 1121.96899 | 618.01868 |
| ENSG00000210112.1 | MT-TM | 0.00000 | 0.00000 | 0.13399 | 0.00000 | 0.00000 | 0.00000 | 0.36393 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.16785 | 0.13935 | 0.34890 | 0.00000 | 0.40153 | 0.74471 | 0.00000 | 0.15755 |
| ENSG00000198763.3 | MT-ND2 | 4228.48145 | 2871.87549 | 2761.64624 | 5103.22412 | 2417.21167 | 2361.06470 | 2872.39697 | 3078.08496 | ⋯ | 2306.98755 | 3569.08911 | 1356.70166 | 1019.21259 | 432.39215 | 960.28937 | 1100.50708 | 2589.74121 | 836.28326 | 544.17621 |
| ENSG00000210117.1 | MT-TW | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.04645 | 0.00000 | 0.00000 | 0.20077 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000210127.1 | MT-TA | 0.12693 | 0.00000 | 0.26410 | 0.26594 | 0.00000 | 0.00000 | 0.00000 | 0.25217 | ⋯ | 1.84237 | 0.21605 | 0.66168 | 0.54931 | 0.22923 | 1.58942 | 1.18715 | 2.20176 | 0.94467 | 0.15526 |
| ENSG00000210135.1 | MT-TN | 0.35993 | 0.43520 | 1.49775 | 1.75956 | 0.49037 | 0.31936 | 1.35601 | 0.59589 | ⋯ | 2.81305 | 4.08428 | 0.78178 | 2.81238 | 1.30000 | 3.14124 | 2.80524 | 7.63077 | 2.97635 | 1.02730 |
| ENSG00000198804.2 | MT-CO1 | 15661.66016 | 15261.75098 | 11303.64844 | 19603.06836 | 2913.64453 | 6795.08105 | 11937.98340 | 7864.29932 | ⋯ | 8555.61133 | 9931.27441 | 3680.04346 | 4611.66553 | 2439.67969 | 5117.47998 | 7931.24316 | 13101.27344 | 4357.16064 | 3762.57886 |
| ENSG00000210151.2 | MT-TS1 | 68.79620 | 87.32864 | 28.91843 | 65.55353 | 9.07903 | 25.45277 | 36.34377 | 22.06525 | ⋯ | 59.09744 | 59.19841 | 19.02321 | 26.87028 | 16.61893 | 27.88717 | 38.78011 | 69.17211 | 28.02511 | 22.66865 |
| ENSG00000210154.1 | MT-TD | 0.12880 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000198712.1 | MT-CO2 | 11730.29785 | 12858.86426 | 11931.20117 | 35615.90234 | 8594.03711 | 7163.49072 | 11873.46191 | 9486.36133 | ⋯ | 7809.95996 | 16700.29102 | 6524.20996 | 6030.77588 | 3623.32739 | 6427.56055 | 8503.33203 | 18922.68164 | 4888.17285 | 4039.84033 |
| ENSG00000210156.1 | MT-TK | 0.00000 | 0.00000 | 0.13016 | 0.00000 | 0.38354 | 0.00000 | 0.11784 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.54258 | 0.00000 | 0.00000 |
| ENSG00000210164.1 | MT-TG | 0.25759 | 0.15573 | 0.40197 | 0.00000 | 0.13161 | 0.00000 | 0.00000 | 0.25588 | ⋯ | 0.43141 | 0.21923 | 0.33570 | 0.00000 | 0.34890 | 0.00000 | 0.20077 | 0.18618 | 0.15976 | 0.15755 |
| ENSG00000198840.2 | MT-ND3 | 1176.81189 | 1009.34979 | 2816.78296 | 7116.30469 | 1049.68384 | 1070.77808 | 1605.18481 | 1925.49426 | ⋯ | 2968.00879 | 5217.97168 | 1408.86121 | 870.05261 | 730.42224 | 1027.34473 | 1461.33569 | 2883.65967 | 995.56390 | 441.25647 |
| ENSG00000210174.1 | MT-TR | 0.00000 | 0.00000 | 0.00000 | 0.14115 | 0.13768 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.30088 | 0.22935 | 0.52680 | 0.09719 | 0.00000 | 0.15338 | 0.00000 | 0.00000 | 0.16713 | 0.16482 |
| ENSG00000210176.1 | MT-TH | 0.00000 | 0.00000 | 0.39614 | 0.26594 | 0.00000 | 0.00000 | 0.23910 | 0.00000 | ⋯ | 0.42516 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.39572 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000210184.1 | MT-TS2 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.13171 | 0.00000 | 0.29492 | ⋯ | 1.16019 | 0.00000 | 0.19346 | 0.05353 | 0.00000 | 0.33797 | 0.23139 | 0.00000 | 0.00000 | 0.00000 |
| ENSG00000210191.1 | MT-TL2 | 0.12335 | 0.00000 | 0.51331 | 1.03378 | 0.25209 | 0.43780 | 0.34855 | 0.24507 | ⋯ | 0.00000 | 0.83987 | 0.00000 | 0.13346 | 0.11138 | 0.14042 | 0.76914 | 0.35662 | 0.30602 | 0.00000 |
| ENSG00000198786.2 | MT-ND5 | 2393.80298 | 1960.96802 | 1896.28870 | 3522.37695 | 1178.93579 | 1345.42151 | 1943.89148 | 1413.57471 | ⋯ | 1770.68628 | 2219.26221 | 684.31268 | 952.33801 | 512.84479 | 972.97974 | 1865.05200 | 2017.67957 | 793.58710 | 757.41357 |
| ENSG00000198695.2 | MT-ND6 | 1825.52332 | 2056.64697 | 2184.88647 | 4568.00195 | 1616.96484 | 1189.86279 | 1906.90405 | 1469.75574 | ⋯ | 1483.81250 | 1337.68286 | 352.41855 | 397.95502 | 310.11151 | 606.44336 | 1043.02661 | 1204.54675 | 593.61139 | 472.01627 |
| ENSG00000210194.1 | MT-TE | 0.25386 | 0.00000 | 0.26410 | 0.00000 | 0.12970 | 0.00000 | 0.00000 | 0.25217 | ⋯ | 0.14172 | 0.43211 | 0.33084 | 0.18310 | 0.22923 | 0.43348 | 0.39572 | 0.18348 | 0.31489 | 0.15526 |
| ENSG00000198727.2 | MT-CYB | 4923.78223 | 3827.00537 | 1905.18481 | 4590.01416 | 2343.92407 | 1902.01685 | 2026.15710 | 1603.98230 | ⋯ | 3631.88745 | 4311.98926 | 1825.74780 | 1135.89490 | 668.37408 | 1153.96252 | 2453.64722 | 3853.68872 | 1645.22168 | 973.56439 |
| ENSG00000210195.2 | MT-TT | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | ⋯ | 0.00000 | 0.00000 | 0.00000 | 0.14357 | 0.00000 | 0.00000 | 0.41370 | 0.19182 | 0.00000 | 0.16232 |
In [262]:
sum(select_cl %in% CCLE_info$cell_line)
657
In [263]:
CCLE_merge_info <- merge(CCLE_info,CCLE_drug,by.x = 'cell_line',by.y='CCLE Cell Line Name',all = F)
In [264]:
select_cl <- CCLE_merge_info %>%
filter(Target == 'EGFR') %>%
filter(tumor_type == 'Lung')
select_cl_name <- select_cl%>%
dplyr::select('cell_line') %>%
t() %>% c()
In [265]:
EGFR_drug_exp <- CCLE_exp %>%
dplyr::select(c('Description',select_cl_name)) %>%
dplyr::filter(Description %in% drivers$V1) %>%
as.data.frame()
Warning message:
“Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(select_cl_name)
# Now:
data %>% select(all_of(select_cl_name))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.”
In [266]:
EGFR_drug_exp
| Description | A549_LUNG | CALU6_LUNG | HARA_LUNG | HCC15_LUNG | HCC827_LUNG | KNS62_LUNG | LCLC103H_LUNG | LUDLU1_LUNG | MORCPR_LUNG | ⋯ | NCIH2170_LUNG | NCIH2172_LUNG | NCIH23_LUNG | NCIH322_LUNG | NCIH441_LUNG | NCIH460_LUNG | NCIH520_LUNG | PC14_LUNG | RERFLCAI_LUNG | SKMES1_LUNG |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| GTF2B | 9.42187 | 9.52591 | 9.82747 | 15.05349 | 20.98313 | 7.69904 | 12.62830 | 5.32157 | 8.86021 | ⋯ | 10.34348 | 10.29332 | 12.15199 | 12.84523 | 15.50471 | 9.35671 | 8.28753 | 11.64325 | 9.09639 | 17.61231 |
| MSH6 | 30.21349 | 16.66706 | 27.83179 | 22.35493 | 13.36039 | 6.35602 | 11.47562 | 15.91921 | 16.38052 | ⋯ | 14.65205 | 13.84182 | 40.61627 | 16.97435 | 28.10175 | 20.41480 | 19.13857 | 20.83116 | 19.96815 | 17.13223 |
| MYO1B | 84.16625 | 12.21347 | 56.12199 | 21.82845 | 53.94776 | 29.39360 | 15.13058 | 24.97976 | 6.97019 | ⋯ | 22.31954 | 31.88694 | 9.43251 | 21.79440 | 27.86195 | 18.75975 | 14.30759 | 22.58268 | 19.45658 | 26.52773 |
| BARD1 | 3.99177 | 3.68420 | 6.96785 | 6.21935 | 3.84172 | 2.16954 | 2.65518 | 3.98600 | 3.47008 | ⋯ | 6.01418 | 5.08219 | 5.91497 | 4.53696 | 12.00526 | 4.24700 | 5.60684 | 3.35062 | 8.43791 | 4.30252 |
| PLK2 | 106.71158 | 13.84899 | 22.49375 | 42.81476 | 80.30707 | 8.90575 | 36.64318 | 30.06351 | 5.89568 | ⋯ | 6.77429 | 64.54214 | 36.31806 | 24.04011 | 3.34105 | 36.31531 | 0.64743 | 96.33008 | 104.66184 | 469.22522 |
| HIST1H1C | 21.23154 | 14.60151 | 28.73901 | 13.71157 | 14.47164 | 14.01782 | 32.72435 | 31.40698 | 47.66726 | ⋯ | 86.00359 | 131.69919 | 31.08146 | 38.50328 | 30.73631 | 7.50665 | 4.84441 | 20.67738 | 11.63652 | 26.83206 |
| CEBPD | 1.38818 | 6.02801 | 2.71927 | 2.79843 | 4.16668 | 2.49031 | 1.67320 | 12.26365 | 18.25912 | ⋯ | 2.76244 | 11.68271 | 2.57368 | 4.38601 | 18.05979 | 2.19177 | 1.65240 | 2.91365 | 2.93413 | 7.44752 |
| TRIB1 | 5.72782 | 7.86291 | 2.70509 | 3.31164 | 5.78330 | 8.91243 | 6.38144 | 5.62817 | 15.39088 | ⋯ | 6.41827 | 4.81647 | 6.84887 | 6.46973 | 8.90075 | 1.51547 | 3.06501 | 22.61054 | 1.08468 | 4.81291 |
| TUBB4B | 231.44135 | 384.12991 | 374.27832 | 210.75525 | 534.51093 | 78.32369 | 217.06387 | 296.64926 | 275.04034 | ⋯ | 216.19600 | 387.98401 | 263.86459 | 639.10437 | 144.75571 | 319.47800 | 158.35429 | 434.68204 | 427.34421 | 191.79034 |
| CD9 | 46.91733 | 38.28646 | 31.07783 | 46.73436 | 95.13908 | 149.23459 | 107.34277 | 149.40462 | 126.39982 | ⋯ | 79.27206 | 19.99291 | 60.11135 | 85.53854 | 218.80959 | 14.52904 | 13.20242 | 116.46647 | 100.11279 | 55.82069 |
| TUBA1B | 1251.16431 | 831.36108 | 1064.56189 | 953.21576 | 1235.05884 | 383.25098 | 701.26965 | 871.77478 | 689.48291 | ⋯ | 604.80829 | 902.41022 | 1239.90430 | 935.79065 | 624.92676 | 1880.50012 | 809.44269 | 680.46375 | 970.42969 | 739.64038 |
| KLF5 | 15.03708 | 10.21180 | 100.12168 | 12.46294 | 15.59129 | 32.08125 | 2.54261 | 78.08482 | 23.68784 | ⋯ | 94.37977 | 9.38432 | 4.42372 | 21.59323 | 23.53358 | 4.31649 | 20.66944 | 18.11410 | 3.96864 | 2.15124 |
| LMO7 | 3.98502 | 5.73257 | 6.22587 | 12.95094 | 11.45193 | 13.01007 | 1.74905 | 7.06538 | 9.95970 | ⋯ | 30.66059 | 1.12792 | 3.76256 | 12.30936 | 51.01361 | 0.84987 | 16.34881 | 12.48705 | 25.55365 | 9.25490 |
| TFDP1 | 33.28401 | 35.17064 | 58.74771 | 27.59179 | 38.70246 | 62.78336 | 16.84071 | 65.83632 | 32.46316 | ⋯ | 101.95407 | 30.29656 | 43.15556 | 72.90529 | 40.46710 | 45.88659 | 36.02008 | 49.30787 | 56.62517 | 40.42910 |
| DAAM1 | 2.93813 | 2.59296 | 3.76573 | 1.97393 | 5.56168 | 6.13586 | 10.96501 | 4.14269 | 3.62285 | ⋯ | 6.79304 | 8.33280 | 3.35902 | 10.16890 | 8.90246 | 0.50112 | 2.86945 | 12.34564 | 1.35333 | 7.29355 |
| HSPA2 | 35.52928 | 8.29720 | 7.73832 | 9.07434 | 1.30731 | 4.76532 | 2.08593 | 5.46101 | 2.72771 | ⋯ | 4.09173 | 132.31943 | 9.44749 | 5.66068 | 1.13168 | 3.74013 | 0.96668 | 5.14966 | 9.94315 | 3.41503 |
| ANXA2 | 905.70850 | 297.84131 | 292.30426 | 351.07166 | 491.56302 | 350.67047 | 336.53204 | 373.70554 | 169.90025 | ⋯ | 251.64005 | 207.98903 | 167.41335 | 361.98868 | 219.16507 | 298.56116 | 103.98492 | 411.26993 | 349.70312 | 898.98889 |
| TPM1 | 207.13792 | 109.33822 | 6.01246 | 7.75077 | 77.59810 | 13.70145 | 64.97376 | 11.77554 | 5.30921 | ⋯ | 1.00032 | 75.56107 | 53.34697 | 46.38272 | 3.00344 | 6.30246 | 6.22729 | 31.33055 | 67.62692 | 48.19003 |
| DGKE | 1.56391 | 1.23536 | 2.69828 | 2.16853 | 2.27285 | 2.91677 | 0.99521 | 2.08815 | 0.38799 | ⋯ | 3.56795 | 1.30310 | 0.49816 | 0.10916 | 3.42770 | 2.08906 | 2.69382 | 2.14484 | 0.07427 | 3.55946 |
| RAB31 | 6.68595 | 15.51748 | 6.88650 | 15.44389 | 8.22771 | 4.91694 | 67.34886 | 4.78308 | 4.37403 | ⋯ | 0.35001 | 10.03424 | 14.98078 | 2.07566 | 6.98250 | 6.76498 | 0.44960 | 14.65951 | 33.87274 | 21.38955 |
| PSMC4 | 173.47475 | 84.36823 | 78.21808 | 79.54345 | 66.41516 | 56.39879 | 71.49141 | 80.96620 | 47.68212 | ⋯ | 111.76073 | 157.40776 | 115.40165 | 94.40004 | 31.04118 | 94.04908 | 77.77753 | 68.05058 | 57.50246 | 65.02472 |
| SLC1A5 | 105.83581 | 74.95303 | 61.44871 | 216.71173 | 28.60874 | 79.93523 | 65.07303 | 105.79476 | 172.34282 | ⋯ | 181.72800 | 38.73779 | 58.71933 | 63.96908 | 83.75444 | 89.03591 | 213.62213 | 80.72714 | 96.84824 | 37.72397 |
| RRBP1 | 29.36313 | 12.82536 | 26.62716 | 25.52167 | 42.59126 | 42.19027 | 38.68987 | 85.32716 | 48.83669 | ⋯ | 25.93232 | 31.93937 | 20.28370 | 24.85145 | 32.55831 | 37.32225 | 6.98978 | 58.63967 | 17.31573 | 49.29018 |
| TGM2 | 70.67321 | 55.75968 | 0.89151 | 247.90877 | 40.08327 | 1.05652 | 48.85313 | 4.81757 | 7.18035 | ⋯ | 4.96301 | 77.50578 | 44.42159 | 83.18359 | 38.66368 | 2.08099 | 0.31467 | 169.11215 | 126.63876 | 102.45492 |
| TFAP2C | 8.42173 | 6.23058 | 3.42565 | 7.07467 | 2.30976 | 6.71867 | 2.96221 | 15.25246 | 21.67481 | ⋯ | 5.78693 | 10.74191 | 35.81564 | 23.67968 | 7.76593 | 3.17164 | 0.08243 | 8.70646 | 1.77203 | 2.53308 |
In [267]:
#利用aggregate函数,对相同的基因名按列取平均
EGFR_drug_exp <- aggregate(.~Description,mean,data=EGFR_drug_exp)
In [268]:
rn <- EGFR_drug_exp$Description
EGFR_drug_exp <- EGFR_drug_exp[,2:ncol(EGFR_drug_exp)]
rownames(EGFR_drug_exp) <- rn
EGFR_drug_exp
| A549_LUNG | CALU6_LUNG | HARA_LUNG | HCC15_LUNG | HCC827_LUNG | KNS62_LUNG | LCLC103H_LUNG | LUDLU1_LUNG | MORCPR_LUNG | NCIH1299_LUNG | ⋯ | NCIH2170_LUNG | NCIH2172_LUNG | NCIH23_LUNG | NCIH322_LUNG | NCIH441_LUNG | NCIH460_LUNG | NCIH520_LUNG | PC14_LUNG | RERFLCAI_LUNG | SKMES1_LUNG | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| ANXA2 | 905.70850 | 297.84131 | 292.30426 | 351.07166 | 491.56302 | 350.67047 | 336.53204 | 373.70554 | 169.90025 | 165.48709 | ⋯ | 251.64005 | 207.98903 | 167.41335 | 361.98868 | 219.16507 | 298.56116 | 103.98492 | 411.26993 | 349.70312 | 898.98889 |
| BARD1 | 3.99177 | 3.68420 | 6.96785 | 6.21935 | 3.84172 | 2.16954 | 2.65518 | 3.98600 | 3.47008 | 4.46638 | ⋯ | 6.01418 | 5.08219 | 5.91497 | 4.53696 | 12.00526 | 4.24700 | 5.60684 | 3.35062 | 8.43791 | 4.30252 |
| CD9 | 46.91733 | 38.28646 | 31.07783 | 46.73436 | 95.13908 | 149.23459 | 107.34277 | 149.40462 | 126.39982 | 64.88426 | ⋯ | 79.27206 | 19.99291 | 60.11135 | 85.53854 | 218.80959 | 14.52904 | 13.20242 | 116.46647 | 100.11279 | 55.82069 |
| CEBPD | 1.38818 | 6.02801 | 2.71927 | 2.79843 | 4.16668 | 2.49031 | 1.67320 | 12.26365 | 18.25912 | 2.75232 | ⋯ | 2.76244 | 11.68271 | 2.57368 | 4.38601 | 18.05979 | 2.19177 | 1.65240 | 2.91365 | 2.93413 | 7.44752 |
| DAAM1 | 2.93813 | 2.59296 | 3.76573 | 1.97393 | 5.56168 | 6.13586 | 10.96501 | 4.14269 | 3.62285 | 1.69593 | ⋯ | 6.79304 | 8.33280 | 3.35902 | 10.16890 | 8.90246 | 0.50112 | 2.86945 | 12.34564 | 1.35333 | 7.29355 |
| DGKE | 1.56391 | 1.23536 | 2.69828 | 2.16853 | 2.27285 | 2.91677 | 0.99521 | 2.08815 | 0.38799 | 3.28413 | ⋯ | 3.56795 | 1.30310 | 0.49816 | 0.10916 | 3.42770 | 2.08906 | 2.69382 | 2.14484 | 0.07427 | 3.55946 |
| GTF2B | 9.42187 | 9.52591 | 9.82747 | 15.05349 | 20.98313 | 7.69904 | 12.62830 | 5.32157 | 8.86021 | 12.71671 | ⋯ | 10.34348 | 10.29332 | 12.15199 | 12.84523 | 15.50471 | 9.35671 | 8.28753 | 11.64325 | 9.09639 | 17.61231 |
| HIST1H1C | 21.23154 | 14.60151 | 28.73901 | 13.71157 | 14.47164 | 14.01782 | 32.72435 | 31.40698 | 47.66726 | 25.25327 | ⋯ | 86.00359 | 131.69919 | 31.08146 | 38.50328 | 30.73631 | 7.50665 | 4.84441 | 20.67738 | 11.63652 | 26.83206 |
| HSPA2 | 35.52928 | 8.29720 | 7.73832 | 9.07434 | 1.30731 | 4.76532 | 2.08593 | 5.46101 | 2.72771 | 7.59667 | ⋯ | 4.09173 | 132.31943 | 9.44749 | 5.66068 | 1.13168 | 3.74013 | 0.96668 | 5.14966 | 9.94315 | 3.41503 |
| KLF5 | 15.03708 | 10.21180 | 100.12168 | 12.46294 | 15.59129 | 32.08125 | 2.54261 | 78.08482 | 23.68784 | 3.95696 | ⋯ | 94.37977 | 9.38432 | 4.42372 | 21.59323 | 23.53358 | 4.31649 | 20.66944 | 18.11410 | 3.96864 | 2.15124 |
| LMO7 | 3.98502 | 5.73257 | 6.22587 | 12.95094 | 11.45193 | 13.01007 | 1.74905 | 7.06538 | 9.95970 | 4.04487 | ⋯ | 30.66059 | 1.12792 | 3.76256 | 12.30936 | 51.01361 | 0.84987 | 16.34881 | 12.48705 | 25.55365 | 9.25490 |
| MSH6 | 30.21349 | 16.66706 | 27.83179 | 22.35493 | 13.36039 | 6.35602 | 11.47562 | 15.91921 | 16.38052 | 22.75378 | ⋯ | 14.65205 | 13.84182 | 40.61627 | 16.97435 | 28.10175 | 20.41480 | 19.13857 | 20.83116 | 19.96815 | 17.13223 |
| MYO1B | 84.16625 | 12.21347 | 56.12199 | 21.82845 | 53.94776 | 29.39360 | 15.13058 | 24.97976 | 6.97019 | 9.03299 | ⋯ | 22.31954 | 31.88694 | 9.43251 | 21.79440 | 27.86195 | 18.75975 | 14.30759 | 22.58268 | 19.45658 | 26.52773 |
| PLK2 | 106.71158 | 13.84899 | 22.49375 | 42.81476 | 80.30707 | 8.90575 | 36.64318 | 30.06351 | 5.89568 | 24.46885 | ⋯ | 6.77429 | 64.54214 | 36.31806 | 24.04011 | 3.34105 | 36.31531 | 0.64743 | 96.33008 | 104.66184 | 469.22522 |
| PSMC4 | 173.47475 | 84.36823 | 78.21808 | 79.54345 | 66.41516 | 56.39879 | 71.49141 | 80.96620 | 47.68212 | 195.19131 | ⋯ | 111.76073 | 157.40776 | 115.40165 | 94.40004 | 31.04118 | 94.04908 | 77.77753 | 68.05058 | 57.50246 | 65.02472 |
| RAB31 | 6.68595 | 15.51748 | 6.88650 | 15.44389 | 8.22771 | 4.91694 | 67.34886 | 4.78308 | 4.37403 | 18.54549 | ⋯ | 0.35001 | 10.03424 | 14.98078 | 2.07566 | 6.98250 | 6.76498 | 0.44960 | 14.65951 | 33.87274 | 21.38955 |
| RRBP1 | 29.36313 | 12.82536 | 26.62716 | 25.52167 | 42.59126 | 42.19027 | 38.68987 | 85.32716 | 48.83669 | 26.05423 | ⋯ | 25.93232 | 31.93937 | 20.28370 | 24.85145 | 32.55831 | 37.32225 | 6.98978 | 58.63967 | 17.31573 | 49.29018 |
| SLC1A5 | 105.83581 | 74.95303 | 61.44871 | 216.71173 | 28.60874 | 79.93523 | 65.07303 | 105.79476 | 172.34282 | 47.80180 | ⋯ | 181.72800 | 38.73779 | 58.71933 | 63.96908 | 83.75444 | 89.03591 | 213.62213 | 80.72714 | 96.84824 | 37.72397 |
| TFAP2C | 8.42173 | 6.23058 | 3.42565 | 7.07467 | 2.30976 | 6.71867 | 2.96221 | 15.25246 | 21.67481 | 0.17790 | ⋯ | 5.78693 | 10.74191 | 35.81564 | 23.67968 | 7.76593 | 3.17164 | 0.08243 | 8.70646 | 1.77203 | 2.53308 |
| TFDP1 | 33.28401 | 35.17064 | 58.74771 | 27.59179 | 38.70246 | 62.78336 | 16.84071 | 65.83632 | 32.46316 | 28.33863 | ⋯ | 101.95407 | 30.29656 | 43.15556 | 72.90529 | 40.46710 | 45.88659 | 36.02008 | 49.30787 | 56.62517 | 40.42910 |
| TGM2 | 70.67321 | 55.75968 | 0.89151 | 247.90877 | 40.08327 | 1.05652 | 48.85313 | 4.81757 | 7.18035 | 101.08371 | ⋯ | 4.96301 | 77.50578 | 44.42159 | 83.18359 | 38.66368 | 2.08099 | 0.31467 | 169.11215 | 126.63876 | 102.45492 |
| TPM1 | 207.13792 | 109.33822 | 6.01246 | 7.75077 | 77.59810 | 13.70145 | 64.97376 | 11.77554 | 5.30921 | 44.48190 | ⋯ | 1.00032 | 75.56107 | 53.34697 | 46.38272 | 3.00344 | 6.30246 | 6.22729 | 31.33055 | 67.62692 | 48.19003 |
| TRIB1 | 5.72782 | 7.86291 | 2.70509 | 3.31164 | 5.78330 | 8.91243 | 6.38144 | 5.62817 | 15.39088 | 6.49076 | ⋯ | 6.41827 | 4.81647 | 6.84887 | 6.46973 | 8.90075 | 1.51547 | 3.06501 | 22.61054 | 1.08468 | 4.81291 |
| TUBA1B | 1251.16431 | 831.36108 | 1064.56189 | 953.21576 | 1235.05884 | 383.25098 | 701.26965 | 871.77478 | 689.48291 | 1067.26819 | ⋯ | 604.80829 | 902.41022 | 1239.90430 | 935.79065 | 624.92676 | 1880.50012 | 809.44269 | 680.46375 | 970.42969 | 739.64038 |
| TUBB4B | 231.44135 | 384.12991 | 374.27832 | 210.75525 | 534.51093 | 78.32369 | 217.06387 | 296.64926 | 275.04034 | 328.42111 | ⋯ | 216.19600 | 387.98401 | 263.86459 | 639.10437 | 144.75571 | 319.47800 | 158.35429 | 434.68204 | 427.34421 | 191.79034 |
In [269]:
EGFR_drug_exp <- log2(EGFR_drug_exp)
In [270]:
EGFR_drug_exp
| A549_LUNG | CALU6_LUNG | HARA_LUNG | HCC15_LUNG | HCC827_LUNG | KNS62_LUNG | LCLC103H_LUNG | LUDLU1_LUNG | MORCPR_LUNG | NCIH1299_LUNG | ⋯ | NCIH2170_LUNG | NCIH2172_LUNG | NCIH23_LUNG | NCIH322_LUNG | NCIH441_LUNG | NCIH460_LUNG | NCIH520_LUNG | PC14_LUNG | RERFLCAI_LUNG | SKMES1_LUNG | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| ANXA2 | 9.8229030 | 8.2184001 | 8.1913270 | 8.4556217 | 8.9412326 | 8.45397214 | 8.394600055 | 8.545758 | 7.408544 | 7.3705749 | ⋯ | 7.9752177437 | 7.7003636 | 7.387271 | 8.499801 | 7.7758741 | 8.2218827 | 6.7002305 | 8.683942 | 8.4499869 | 9.812159 |
| BARD1 | 1.9970286 | 1.8813514 | 2.8007136 | 2.6367638 | 1.9417524 | 1.11738919 | 1.408809668 | 1.994942 | 1.794969 | 2.1591060 | ⋯ | 2.5883680474 | 2.3454503 | 2.564371 | 2.181726 | 3.5855947 | 2.0864441 | 2.4871879 | 1.744428 | 3.0768857 | 2.105182 |
| CD9 | 5.5520490 | 5.2587624 | 4.9578139 | 5.5464117 | 6.5719662 | 7.22143816 | 6.746081212 | 7.223081 | 6.981851 | 6.0197966 | ⋯ | 6.3087405623 | 4.3214166 | 5.909566 | 6.418503 | 7.7735322 | 3.8608675 | 3.7227305 | 6.863771 | 6.6454825 | 5.802728 |
| CEBPD | 0.4731946 | 2.5916818 | 1.4432194 | 1.4846177 | 2.0588983 | 1.31632534 | 0.742609903 | 3.616317 | 4.190545 | 1.4606482 | ⋯ | 1.4659431296 | 3.5463031 | 1.363833 | 2.132909 | 4.1747092 | 1.1320964 | 0.7245630 | 1.542828 | 1.5529328 | 2.896760 |
| DAAM1 | 1.5548982 | 1.3746000 | 1.9129296 | 0.9810708 | 2.4755207 | 2.61726557 | 3.454835223 | 2.050568 | 1.857125 | 0.7620766 | ⋯ | 2.7640573493 | 3.0588014 | 1.748040 | 3.346092 | 3.1542040 | -0.9967720 | 1.5207742 | 3.625930 | 0.4365137 | 2.866621 |
| DGKE | 0.6451575 | 0.3049315 | 1.4320401 | 1.1167174 | 1.1845025 | 1.54437163 | -0.006927113 | 1.062225 | -1.365909 | 1.7155112 | ⋯ | 1.8350953980 | 0.3819478 | -1.005319 | -3.195484 | 1.7772408 | 1.0628539 | 1.4296535 | 1.100870 | -3.7510766 | 1.831658 |
| GTF2B | 3.2360134 | 3.2518569 | 3.2968201 | 3.9120261 | 4.3911580 | 2.94467857 | 3.658588534 | 2.411852 | 3.147341 | 3.6686536 | ⋯ | 3.3706497481 | 3.3636365 | 3.603121 | 3.683161 | 3.9546346 | 3.2260013 | 3.0509422 | 3.541422 | 3.1852941 | 4.138512 |
| HIST1H1C | 4.4081371 | 3.8680457 | 4.8449385 | 3.7773219 | 3.8551565 | 3.80919010 | 5.032292631 | 4.973013 | 5.574927 | 4.6583983 | ⋯ | 6.4263249776 | 7.0411027 | 4.957982 | 5.266909 | 4.9418721 | 2.9081692 | 2.2763210 | 4.369981 | 3.5405878 | 4.745886 |
| HSPA2 | 5.1509365 | 3.0526246 | 2.9520204 | 3.1817927 | 0.3866013 | 2.25257310 | 1.060690744 | 2.449168 | 1.447690 | 2.9253672 | ⋯ | 2.0327109495 | 7.0478811 | 3.239931 | 2.500975 | 0.1784661 | 1.9030884 | -0.0488897 | 2.364477 | 3.3137030 | 1.771898 |
| KLF5 | 3.9104525 | 3.3521653 | 6.6456106 | 3.6395725 | 3.9626684 | 5.00365845 | 1.346310190 | 6.286970 | 4.566075 | 1.9843925 | ⋯ | 6.5604057506 | 3.2302522 | 2.145260 | 4.432507 | 4.5566489 | 2.1098586 | 4.3694274 | 4.179041 | 1.9886447 | 1.105168 |
| LMO7 | 1.9945870 | 2.5191821 | 2.6382755 | 3.6949849 | 3.5175189 | 3.70155682 | 0.806571532 | 2.820767 | 3.316102 | 2.0160933 | ⋯ | 4.9383135539 | 0.1736647 | 1.911715 | 3.621684 | 5.6728103 | -0.2346859 | 4.0311137 | 3.642361 | 4.6754575 | 3.210217 |
| MSH6 | 4.9171209 | 4.0589277 | 4.7986618 | 4.4825211 | 3.7398902 | 2.66812366 | 3.520500196 | 3.992697 | 4.033909 | 4.5080343 | ⋯ | 3.8730306242 | 3.7909617 | 5.343986 | 4.085284 | 4.8125881 | 4.3515435 | 4.2584111 | 4.380671 | 4.3196288 | 4.098641 |
| MYO1B | 6.3951699 | 3.6104012 | 5.8104943 | 4.4481378 | 5.7534912 | 4.87743016 | 3.919395386 | 4.642688 | 2.801198 | 3.1752036 | ⋯ | 4.4802353888 | 4.9948938 | 3.237642 | 4.445886 | 4.8002243 | 4.2295687 | 3.8387088 | 4.497145 | 4.2821862 | 4.729429 |
| PLK2 | 6.7375729 | 3.7917089 | 4.4914523 | 5.4200363 | 6.3274551 | 3.15473711 | 5.195472805 | 4.909942 | 2.559658 | 4.6128744 | ⋯ | 2.7600697484 | 6.0121695 | 5.182615 | 4.587372 | 1.7403016 | 5.1825060 | -0.6272039 | 6.589914 | 6.7095917 | 8.874137 |
| PSMC4 | 7.4385819 | 6.3986279 | 6.2894302 | 6.3136712 | 6.0534407 | 5.81759231 | 6.159698001 | 6.339248 | 5.575376 | 7.6087450 | ⋯ | 6.8042695391 | 7.2983629 | 6.850520 | 6.560716 | 4.9561115 | 6.5553419 | 6.2812815 | 6.088536 | 5.8455518 | 6.022916 |
| RAB31 | 2.7411326 | 3.9558224 | 2.7837709 | 3.9489643 | 3.0404909 | 2.29776075 | 6.073581621 | 2.257940 | 2.128963 | 4.2129965 | ⋯ | -1.5145319536 | 3.3268594 | 3.905041 | 1.053570 | 2.8037437 | 2.7580857 | -1.1532861 | 3.873765 | 5.0820528 | 4.418834 |
| RRBP1 | 4.8759339 | 3.6809274 | 4.7348267 | 4.6736508 | 5.4124855 | 5.39883842 | 5.273883976 | 6.414933 | 5.609894 | 4.7034457 | ⋯ | 4.6966793756 | 4.9972640 | 4.342249 | 4.635258 | 5.0249539 | 5.2219641 | 2.8052470 | 5.873805 | 4.1140113 | 5.623228 |
| SLC1A5 | 6.7256840 | 6.2279149 | 5.9413108 | 7.7596334 | 4.8383841 | 6.32075958 | 6.023987826 | 6.725124 | 7.429137 | 5.5789930 | ⋯ | 7.5056369118 | 5.2756697 | 5.875764 | 5.999303 | 6.3880938 | 6.4763154 | 7.7389173 | 6.334982 | 6.5976539 | 5.237410 |
| TFAP2C | 3.0741166 | 2.6393665 | 1.7763778 | 2.8226629 | 1.2077430 | 2.74817567 | 1.566673921 | 3.930970 | 4.437947 | -2.4908616 | ⋯ | 2.5327981931 | 3.4251786 | 5.162518 | 4.565578 | 2.9571587 | 1.6652290 | -3.6006867 | 3.122086 | 0.8254030 | 1.340893 |
| TFDP1 | 5.0567574 | 5.1362997 | 5.8764607 | 4.7861671 | 5.2743534 | 5.97231034 | 4.073881058 | 6.040812 | 5.020732 | 4.8246981 | ⋯ | 6.6717755586 | 4.9210821 | 5.431475 | 6.187952 | 5.3386776 | 5.5200007 | 5.1707295 | 5.623746 | 5.8233716 | 5.337322 |
| TGM2 | 6.1430915 | 5.8011504 | -0.1656771 | 7.9536655 | 5.3249283 | 0.07932008 | 5.610379093 | 2.268306 | 2.844054 | 6.6594067 | ⋯ | 2.3112153615 | 6.2762320 | 5.473189 | 6.378227 | 5.2729071 | 1.0572700 | -1.6680885 | 7.401837 | 6.9845752 | 6.678845 |
| TPM1 | 7.6944479 | 6.7726540 | 2.5879554 | 2.9543396 | 6.2779494 | 3.77625667 | 6.021785291 | 3.557721 | 2.408497 | 5.4751465 | ⋯ | 0.0004615886 | 6.2395712 | 5.737334 | 5.535516 | 1.5866158 | 2.6559151 | 2.6386045 | 4.969498 | 6.0795257 | 5.590663 |
| TRIB1 | 2.5179862 | 2.9750633 | 1.4356766 | 1.7275458 | 2.5318929 | 3.15581884 | 2.673882011 | 2.492666 | 3.944004 | 2.6983874 | ⋯ | 2.6821844813 | 2.2679762 | 2.775866 | 2.693706 | 3.1539269 | 0.5997653 | 1.6158918 | 4.498924 | 0.1172695 | 2.266909 |
| TUBA1B | 10.2890555 | 9.6993314 | 10.0560441 | 9.8966590 | 10.2703641 | 8.58214567 | 9.453825481 | 9.767812 | 9.429371 | 10.0597070 | ⋯ | 9.2403341042 | 9.8176396 | 10.276013 | 9.870042 | 9.2875433 | 10.8769007 | 9.6607851 | 9.410374 | 9.9224799 | 9.530680 |
| TUBB4B | 7.8545028 | 8.5854505 | 8.5479677 | 7.7194248 | 9.0620756 | 6.29137683 | 7.761975801 | 8.212614 | 8.103499 | 8.3594031 | ⋯ | 7.7561960207 | 8.5998534 | 8.043654 | 9.319908 | 7.1774764 | 8.3195728 | 7.3070121 | 8.763817 | 8.7392548 | 7.583386 |
In [271]:
EGFR_drug_exp$V1 = rownames(EGFR_drug_exp)
In [272]:
score_df <- merge(EGFR_drug_exp,drivers,by = 'V1',all = F)
score_df
| V1 | A549_LUNG | CALU6_LUNG | HARA_LUNG | HCC15_LUNG | HCC827_LUNG | KNS62_LUNG | LCLC103H_LUNG | LUDLU1_LUNG | MORCPR_LUNG | ⋯ | weight_grad_total_dir_mean | counts | is_tf | is_in_FAM | is_in_ROS | is_in_Pathway | rank_shap_weight | rank_grad_weight | directions | directions_cal |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <int> | <lgl> | <lgl> | <lgl> | <lgl> | <dbl> | <dbl> | <lgl> | <dbl> |
| ANXA2 | 9.8229030 | 8.2184001 | 8.1913270 | 8.4556217 | 8.9412326 | 8.45397214 | 8.394600055 | 8.545758 | 7.408544 | ⋯ | -7.150776e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 8 | 14 | FALSE | -1 |
| BARD1 | 1.9970286 | 1.8813514 | 2.8007136 | 2.6367638 | 1.9417524 | 1.11738919 | 1.408809668 | 1.994942 | 1.794969 | ⋯ | 1.084314e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 2 | 7 | TRUE | 1 |
| CD9 | 5.5520490 | 5.2587624 | 4.9578139 | 5.5464117 | 6.5719662 | 7.22143816 | 6.746081212 | 7.223081 | 6.981851 | ⋯ | -1.463574e-04 | 6 | FALSE | FALSE | FALSE | FALSE | 3 | 4 | FALSE | -1 |
| CEBPD | 0.4731946 | 2.5916818 | 1.4432194 | 1.4846177 | 2.0588983 | 1.31632534 | 0.742609903 | 3.616317 | 4.190545 | ⋯ | 7.729752e-05 | 9 | TRUE | FALSE | FALSE | FALSE | 20 | 11 | TRUE | 1 |
| DAAM1 | 1.5548982 | 1.3746000 | 1.9129296 | 0.9810708 | 2.4755207 | 2.61726557 | 3.454835223 | 2.050568 | 1.857125 | ⋯ | 1.041791e-04 | 4 | FALSE | FALSE | FALSE | FALSE | 4 | 10 | TRUE | 1 |
| DGKE | 0.6451575 | 0.3049315 | 1.4320401 | 1.1167174 | 1.1845025 | 1.54437163 | -0.006927113 | 1.062225 | -1.365909 | ⋯ | -5.831443e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 13 | 17 | FALSE | -1 |
| GTF2B | 3.2360134 | 3.2518569 | 3.2968201 | 3.9120261 | 4.3911580 | 2.94467857 | 3.658588534 | 2.411852 | 3.147341 | ⋯ | -1.316672e-04 | 10 | TRUE | FALSE | FALSE | FALSE | 11 | 6 | FALSE | -1 |
| HIST1H1C | 4.4081371 | 3.8680457 | 4.8449385 | 3.7773219 | 3.8551565 | 3.80919010 | 5.032292631 | 4.973013 | 5.574927 | ⋯ | -7.409843e-05 | 4 | FALSE | FALSE | FALSE | FALSE | 23 | 13 | FALSE | -1 |
| HSPA2 | 5.1509365 | 3.0526246 | 2.9520204 | 3.1817927 | 0.3866013 | 2.25257310 | 1.060690744 | 2.449168 | 1.447690 | ⋯ | -2.506919e-04 | 8 | FALSE | FALSE | FALSE | FALSE | 17 | 1 | FALSE | -1 |
| KLF5 | 3.9104525 | 3.3521653 | 6.6456106 | 3.6395725 | 3.9626684 | 5.00365845 | 1.346310190 | 6.286970 | 4.566075 | ⋯ | 1.071805e-04 | 6 | TRUE | FALSE | FALSE | FALSE | 24 | 9 | TRUE | 1 |
| LMO7 | 1.9945870 | 2.5191821 | 2.6382755 | 3.6949849 | 3.5175189 | 3.70155682 | 0.806571532 | 2.820767 | 3.316102 | ⋯ | 2.352714e-04 | 6 | FALSE | FALSE | FALSE | FALSE | 21 | 2 | TRUE | 1 |
| MSH6 | 4.9171209 | 4.0589277 | 4.7986618 | 4.4825211 | 3.7398902 | 2.66812366 | 3.520500196 | 3.992697 | 4.033909 | ⋯ | 7.425508e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 6 | 12 | TRUE | 1 |
| MYO1B | 6.3951699 | 3.6104012 | 5.8104943 | 4.4481378 | 5.7534912 | 4.87743016 | 3.919395386 | 4.642688 | 2.801198 | ⋯ | 1.748662e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 12 | 3 | TRUE | 1 |
| PLK2 | 6.7375729 | 3.7917089 | 4.4914523 | 5.4200363 | 6.3274551 | 3.15473711 | 5.195472805 | 4.909942 | 2.559658 | ⋯ | 6.720297e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 10 | 15 | TRUE | 1 |
| PSMC4 | 7.4385819 | 6.3986279 | 6.2894302 | 6.3136712 | 6.0534407 | 5.81759231 | 6.159698001 | 6.339248 | 5.575376 | ⋯ | -4.451585e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 14 | 20 | FALSE | -1 |
| RAB31 | 2.7411326 | 3.9558224 | 2.7837709 | 3.9489643 | 3.0404909 | 2.29776075 | 6.073581621 | 2.257940 | 2.128963 | ⋯ | -5.959134e-05 | 8 | FALSE | FALSE | FALSE | FALSE | 9 | 16 | FALSE | -1 |
| RRBP1 | 4.8759339 | 3.6809274 | 4.7348267 | 4.6736508 | 5.4124855 | 5.39883842 | 5.273883976 | 6.414933 | 5.609894 | ⋯ | -1.221697e-05 | 9 | FALSE | FALSE | FALSE | FALSE | 18 | 23 | FALSE | -1 |
| SLC1A5 | 6.7256840 | 6.2279149 | 5.9413108 | 7.7596334 | 4.8383841 | 6.32075958 | 6.023987826 | 6.725124 | 7.429137 | ⋯ | 1.082030e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 1 | 8 | TRUE | 1 |
| TFAP2C | 3.0741166 | 2.6393665 | 1.7763778 | 2.8226629 | 1.2077430 | 2.74817567 | 1.566673921 | 3.930970 | 4.437947 | ⋯ | 1.130995e-05 | 7 | TRUE | FALSE | FALSE | FALSE | 26 | 25 | TRUE | 1 |
| TFDP1 | 5.0567574 | 5.1362997 | 5.8764607 | 4.7861671 | 5.2743534 | 5.97231034 | 4.073881058 | 6.040812 | 5.020732 | ⋯ | -5.785423e-05 | 10 | TRUE | FALSE | FALSE | FALSE | 7 | 18 | FALSE | -1 |
| TGM2 | 6.1430915 | 5.8011504 | -0.1656771 | 7.9536655 | 5.3249283 | 0.07932008 | 5.610379093 | 2.268306 | 2.844054 | ⋯ | 5.989809e-06 | 10 | FALSE | FALSE | FALSE | FALSE | 16 | 26 | TRUE | 1 |
| TPM1 | 7.6944479 | 6.7726540 | 2.5879554 | 2.9543396 | 6.2779494 | 3.77625667 | 6.021785291 | 3.557721 | 2.408497 | ⋯ | 2.991494e-05 | 6 | FALSE | FALSE | FALSE | FALSE | 25 | 21 | TRUE | 1 |
| TRIB1 | 2.5179862 | 2.9750633 | 1.4356766 | 1.7275458 | 2.5318929 | 3.15581884 | 2.673882011 | 2.492666 | 3.944004 | ⋯ | 2.118026e-05 | 6 | FALSE | FALSE | FALSE | FALSE | 22 | 22 | TRUE | 1 |
| TUBA1B | 10.2890555 | 9.6993314 | 10.0560441 | 9.8966590 | 10.2703641 | 8.58214567 | 9.453825481 | 9.767812 | 9.429371 | ⋯ | -1.412048e-04 | 10 | FALSE | FALSE | FALSE | FALSE | 5 | 5 | FALSE | -1 |
| TUBB4B | 7.8545028 | 8.5854505 | 8.5479677 | 7.7194248 | 9.0620756 | 6.29137683 | 7.761975801 | 8.212614 | 8.103499 | ⋯ | 1.148352e-05 | 10 | FALSE | FALSE | FALSE | FALSE | 19 | 24 | TRUE | 1 |
In [273]:
FUN1 <- function(x){
sum(x*score_df$weight_shap_total_mean)
#sum(x)
}
score <- apply(score_df[,2:32],MARGIN = 2,FUN = FUN1)
In [274]:
score
- A549_LUNG
- 0.134837478227318
- CALU6_LUNG
- 0.120836389809897
- HARA_LUNG
- 0.1230817780117
- HCC15_LUNG
- 0.130704018944912
- HCC827_LUNG
- 0.129378238192507
- KNS62_LUNG
- 0.116708334606072
- LCLC103H_LUNG
- 0.124359672248607
- LUDLU1_LUNG
- 0.129448281875588
- MORCPR_LUNG
- 0.11897644840739
- NCIH1299_LUNG
- 0.121988442361029
- NCIH1581_LUNG
- 0.0887077652188179
- NCIH1648_LUNG
- 0.133232983835933
- NCIH1650_LUNG
- 0.143886772763713
- NCIH1693_LUNG
- 0.124858004409791
- NCIH1703_LUNG
- 0.122857753065183
- NCIH1792_LUNG
- 0.130016488732899
- NCIH1944_LUNG
- 0.117469879237864
- NCIH2023_LUNG
- 0.1351739855138
- NCIH2030_LUNG
- 0.126989230159551
- NCIH2087_LUNG
- 0.130160035769625
- NCIH2122_LUNG
- 0.121636385413356
- NCIH2170_LUNG
- 0.12365955316306
- NCIH2172_LUNG
- 0.128340693576546
- NCIH23_LUNG
- 0.125817370868064
- NCIH322_LUNG
- 0.127670908043556
- NCIH441_LUNG
- 0.132165281974901
- NCIH460_LUNG
- 0.109316837775914
- NCIH520_LUNG
- 0.0978809119795495
- PC14_LUNG
- 0.13793056567663
- RERFLCAI_LUNG
- 0.127522769035006
- SKMES1_LUNG
- 0.134541775398636
In [275]:
select_cl
| cell_line | n_replicates | clean_cell_line_name | cell_line_SSMD | SSMD_failure | culture_type | culture_medium | culture_code | aliases | primary_tissue | ⋯ | Target | Doses (uM) | Activity Data (median) | Activity SD | Num Data | FitType | EC50 (uM) | IC50 (uM) | Amax | ActArea |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <int> | <chr> | <dbl> | <lgl> | <chr> | <chr> | <chr> | <chr> | <chr> | ⋯ | <chr> | <chr> | <chr> | <chr> | <int> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> |
| A549_LUNG | 4 | A549 | -1.939855 | FALSE | Adherent | DMEM; 10% FBS | DMEM001 | A549 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.41,-.17,-9.7,-12,-5.8,-25,-29,-77 | 11.7,7.54,13.8,5.52,20.7,2.17,8.75,6.10 | 8 | Sigmoid | 8.69271626 | 4.22724628 | -75.1556473 | 0.6707 |
| A549_LUNG | 4 | A549 | -1.939855 | FALSE | Adherent | DMEM; 10% FBS | DMEM001 | A549 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -16,-7.4,-10,-7.1,-9.5,-12,-27,-37 | 2.25,2.07,6.74,1.32,1.54,.97,3.71,3.82 | 8 | Sigmoid | 2.06042409 | 8.00000000 | -36.7739677 | 0.4353 |
| A549_LUNG | 4 | A549 | -1.939855 | FALSE | Adherent | DMEM; 10% FBS | DMEM001 | A549 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -3.6,-1.1,4.70,-.63,3.12,-11,-25,-43 | .61,1.43,5.41,.84,3.71,0,4.15,6.68 | 8 | Sigmoid | 2.39471769 | 8.00000000 | -43.0836754 | 0.4884 |
| CALU6_LUNG | 2 | CALU6 | -1.061810 | FALSE | EMEM: 90.0% 10%FBS | Calu-6 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -17,-2.6,8.33,-19,5.39,-2.4,-10,-20 | 27.9,5.25,9.48,17.0,6.09,2.24,6.38,6.08 | 8 | Constant | NA | 8.00000000 | -4.8355103 | 0.1923 | ||
| CALU6_LUNG | 2 | CALU6 | -1.061810 | FALSE | EMEM: 90.0% 10%FBS | Calu-6 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -7.4,-19,-13,7.88,.92,-7.6,-7.3,-18 | 1.28,4.50,26.7,36.8,.43,13.8,2.84,11.2 | 8 | Constant | NA | 8.00000000 | -8.6999998 | 0.7187 | ||
| CALU6_LUNG | 2 | CALU6 | -1.061810 | FALSE | EMEM: 90.0% 10%FBS | Calu-6 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 15.5,-2.8,18.9,16.3,15.8,-4.6,3.92,-53 | 11.8,8.28,9.89,6.67,8.08,7.41,6.10,3.58 | 8 | Sigmoid | 8.70410325 | 7.80798435 | -51.7345047 | 0.4963 | ||
| HARA_LUNG | 2 | HARA | -1.372030 | FALSE | RPMI; 10% FBS | HARA | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 3.54,3.77,5.40,-3.6,-5.6,-7.5,-27,-56 | 24.1,9.97,8.00,3.19,4.96,4.41,.95,2.06 | 8 | Sigmoid | 8.86180042 | 6.65674067 | -56.2591362 | 0.7284 | ||
| HARA_LUNG | 2 | HARA | -1.372030 | FALSE | RPMI; 10% FBS | HARA | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -16,-20,-19,-13,.50,-1.9,-35,-46 | 12.7,4.09,19.3,7.30,9.93,4.63,2.85,5.75 | 8 | Sigmoid | 2.15157342 | 8.00000000 | -45.9132690 | 1.1713 | ||
| HARA_LUNG | 2 | HARA | -1.372030 | FALSE | RPMI; 10% FBS | HARA | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.60,-.54,-1.5,.12,-6.1,-14,-42,-44 | 13.4,10.6,8.83,1.36,18.6,14.6,.92,.43 | 8 | Sigmoid | 1.00138474 | 8.00000000 | -44.4066467 | 0.8434 | ||
| HCC15_LUNG | 2 | HCC15 | -2.516822 | FALSE | RPMI; 10% FBS | HCC15 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.8,-2.8,-7.8,6.21,-9.0,-4.3,-11,-8.1 | 24.5,8.60,14.8,8.00,14.0,13.3,22.0,3.79 | 8 | Constant | NA | 8.00000000 | -2.6100700 | 0.6883 | ||
| HCC15_LUNG | 2 | HCC15 | -2.516822 | FALSE | RPMI; 10% FBS | HCC15 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 5.44,-2.3,6.74,-.19,10.5,-3.1,-16,-30 | .14,3.17,3.55,9.27,.41,7.36,9.64,2.32 | 8 | Sigmoid | 2.19705582 | 8.00000000 | -30.3248806 | 0.9135 | ||
| HCC15_LUNG | 2 | HCC15 | -2.516822 | FALSE | RPMI; 10% FBS | HCC15 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -3.0,.70,1.30,-7.1,-1.1,-7.3,1.14,-7.0 | 20.6,6.42,20.1,17.3,18.1,4.82,7.12,3.08 | 8 | Constant | NA | 8.00000000 | -3.8652062 | 0.3449 | ||
| HCC827_LUNG | 3 | HCC827 | -2.097436 | FALSE | adherentepithelialdensity >30% | RPMI; 10% FBS | RPMI001 | HCC827 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.5,23.7,-12,23.9,-4.4,.62,-19,-81 | 12.3,5.57,9.59,9.98,17.1,8.29,29.2,2.66 | 8 | Linear | NA | 0.37213399 | -81.4721680 | 0.9560 |
| HCC827_LUNG | 3 | HCC827 | -2.097436 | FALSE | adherentepithelialdensity >30% | RPMI; 10% FBS | RPMI001 | HCC827 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -18,4.02,-21,8.25,-44,-75,-89,-89 | 7.20,17.6,21.9,17.3,4.25,4.89,3.19,1.47 | 8 | Sigmoid | 0.30715630 | 0.31740648 | -88.9998322 | 3.1181 |
| HCC827_LUNG | 3 | HCC827 | -2.097436 | FALSE | adherentepithelialdensity >30% | RPMI; 10% FBS | RPMI001 | HCC827 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.85,1.47,-33,-72,-83,-80,-86,-90 | 26.3,14.2,14.2,5.34,.98,6.57,9.09,.13 | 8 | Sigmoid | 0.02933497 | 0.03891792 | -85.4799652 | 4.4132 |
| KNS62_LUNG | 2 | KNS62 | -2.008283 | FALSE | MEMalpha; 10% FBS | KNS-62 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.89,12.2,13.3,8.62,22.9,1.10,-7.1,-31 | 19.6,19.8,1.31,18.3,15.3,11.9,14.6,23.4 | 8 | Sigmoid | 3.77516007 | 8.00000000 | -30.0501022 | 0.2334 | ||
| KNS62_LUNG | 2 | KNS62 | -2.008283 | FALSE | MEMalpha; 10% FBS | KNS-62 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -19,-28,-18,-17,15.4,-13,-16,-11 | 14.7,32.0,3.36,22.8,26.9,16.6,9.39,2.29 | 8 | Constant | NA | 8.00000000 | -10.8984327 | 1.0744 | ||
| KNS62_LUNG | 2 | KNS62 | -2.008283 | FALSE | MEMalpha; 10% FBS | KNS-62 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 8.28,20.5,-2.4,5.25,-7.9,-21,-1.6,23.1 | 14.6,2.50,33.5,8.66,1.92,2.23,.74,34.6 | 8 | Constant | NA | 8.00000000 | -0.8061247 | 0.1777 | ||
| LCLC103H_LUNG | 2 | LCLC103H | -1.292554 | FALSE | RPMI;10% FBS; | LCLC-103H | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | .84,4.48,.80,1.30,-.87,-2.8,3.90,-12 | 17.5,3.10,1.19,1.22,15.7,8.88,1.05,1.26 | 8 | Constant | NA | 8.00000000 | 1.8560940 | 0.1241 | ||
| LCLC103H_LUNG | 2 | LCLC103H | -1.292554 | FALSE | RPMI;10% FBS; | LCLC-103H | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -20,-16,-15,-22,-11,-16,-18,-13 | 10.6,4.63,12.8,3.42,10.4,4.44,8.28,8.23 | 8 | Constant | NA | 8.00000000 | -18.3372269 | 1.4051 | ||
| LCLC103H_LUNG | 2 | LCLC103H | -1.292554 | FALSE | RPMI;10% FBS; | LCLC-103H | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 1.09,-3.8,1.02,3.71,1.54,4.05,-5.1,-15 | 4.09,11.2,11.3,15.6,.85,23.1,11.4,7.10 | 8 | Constant | NA | 8.00000000 | -0.3308865 | 0.2600 | ||
| LUDLU1_LUNG | 2 | LUDLU1 | -1.201608 | FALSE | Adherent | RPMI; 10% FBS | LUDLU-1 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 11.8,12.6,-17,-8.8,-30,-34,-40,-61 | 6.41,17.3,22.2,12.0,16.2,23.5,26.6,16.1 | 8 | Linear | NA | 2.31964210 | -61.4747848 | 1.8359 | |
| LUDLU1_LUNG | 2 | LUDLU1 | -1.201608 | FALSE | Adherent | RPMI; 10% FBS | LUDLU-1 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -6.6,-9.9,-1.7,-21,2.63,-35,-50,-67 | 11.1,17.4,5.29,17.7,10.3,9.18,12.2,8.80 | 8 | Sigmoid | 1.01615298 | 1.82598734 | -64.4585876 | 1.5094 | |
| LUDLU1_LUNG | 2 | LUDLU1 | -1.201608 | FALSE | Adherent | RPMI; 10% FBS | LUDLU-1 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 14.0,5.44,2.74,-13,-10,-48,-52,-64 | 18.7,48.1,13.4,9.06,14.2,3.47,9.53,3.34 | 8 | Sigmoid | 0.33664727 | 1.65249217 | -63.6434669 | 1.5519 | |
| MORCPR_LUNG | 3 | MORCPR | -1.926500 | FALSE | MOR/CPR | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 14.3,3.18,13.4,-.22,13.3,-5.9,-20,-26 | 5.66,1.54,7.53,2.11,14.8,3.87,13.9,.41 | 8 | Sigmoid | 1.09860432 | 8.00000000 | -26.0566044 | 0.3634 | |||
| MORCPR_LUNG | 3 | MORCPR | -1.926500 | FALSE | MOR/CPR | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -27,-8.5,4.08,-12,-17,6.88,-10,-27 | 12.0,16.0,4.67,.38,21.4,6.52,5.94,1.47 | 8 | Constant | NA | 8.00000000 | -12.3387585 | 0.7898 | |||
| MORCPR_LUNG | 3 | MORCPR | -1.926500 | FALSE | MOR/CPR | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.61,-13,2.33,2.81,-12,-7.1,-4.8,-17 | 2.42,21.5,.53,12.7,6.18,4.69,18.2,5.87 | 8 | Constant | NA | 8.00000000 | -4.8009200 | 0.2041 | |||
| NCIH1299_LUNG | 2 | NCIH1299 | -1.197529 | FALSE | Adherent | RPMI; 10% FBS | NCI-H1299 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 7.14,22.3,16.5,5.65,8.67,.16,-19,-9.0 | 2.10,14.6,1.45,21.6,5.31,13.2,27.8,13.8 | 8 | Sigmoid | 0.61638433 | 8.00000000 | -9.6427193 | 0.2706 | |
| NCIH1299_LUNG | 2 | NCIH1299 | -1.197529 | FALSE | Adherent | RPMI; 10% FBS | NCI-H1299 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.2,7.69,-18,-7.6,-.17,-15,-11,-26 | 11.2,9.86,18.8,7.36,17.6,10.3,11.2,8.84 | 8 | Constant | NA | 8.00000000 | -8.2650490 | 0.7984 | |
| NCIH1299_LUNG | 2 | NCIH1299 | -1.197529 | FALSE | Adherent | RPMI; 10% FBS | NCI-H1299 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 16.9,9.94,9.77,-3.0,4.30,-12,-15,-43 | 13.0,.64,.78,3.41,2.87,2.38,9.43,.10 | 8 | Sigmoid | 8.62741137 | 8.00000000 | -40.9888763 | 0.7121 | |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| NCIH2170_LUNG | 3 | NCIH2170 | -1.204793 | FALSE | RPMI;10% FBS; | NCI-H2170 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | .11,14.4,-15,-18,-43,-74,-83,-86 | 10.0,14.5,5.16,17.9,6.69,14.4,8.60,14.6 | 8 | Sigmoid | 0.22801438 | 0.30098090 | -88.1178818 | 3.14510 | ||
| NCIH2170_LUNG | 3 | NCIH2170 | -1.204793 | FALSE | RPMI;10% FBS; | NCI-H2170 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -4.6,-23,6.94,-.55,-20,-22,-50,-73 | 5.78,15.4,2.42,13.5,7.68,8.98,8.04,.93 | 8 | Sigmoid | 2.74079251 | 2.65564489 | -72.9412613 | 1.89220 | ||
| NCIH2170_LUNG | 3 | NCIH2170 | -1.204793 | FALSE | RPMI;10% FBS; | NCI-H2170 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 7.61,-.16,27.6,12.1,-14,-28,-60,-84 | 11.0,8.38,15.7,9.31,7.45,29.0,10.8,8.37 | 8 | Sigmoid | 1.18077564 | 1.56435514 | -82.4917068 | 2.03070 | ||
| NCIH2172_LUNG | 2 | NCIH2172 | -1.750465 | FALSE | RPMI-1640: 90.0% | NCI-H2172 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -2.2,-12,-1.4,5.69,12.9,1.65,12.2,-23 | 6.86,19.6,11.3,15.5,24.5,27.7,12.0,35.6 | 8 | Constant | NA | 8.00000000 | 0.9903433 | 0.03765 | ||
| NCIH2172_LUNG | 2 | NCIH2172 | -1.750465 | FALSE | RPMI-1640: 90.0% | NCI-H2172 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -74,-13,-21,1.99,-11,18.4,-49,-9.5 | 18.3,32.2,13.6,19.7,24.3,18.5,15.7,23.1 | 8 | Constant | NA | 8.00000000 | -18.4410305 | 0.44160 | ||
| NCIH2172_LUNG | 2 | NCIH2172 | -1.750465 | FALSE | RPMI-1640: 90.0% | NCI-H2172 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 32.7,-21,18.1,-39,12.0,-33,13.4,-36 | 60.6,29.2,54.4,11.1,66.9,19.3,22.7,9.33 | 8 | Constant | NA | 8.00000000 | -19.3613071 | 0.19800 | ||
| NCIH23_LUNG | 4 | NCIH23 | -2.615303 | FALSE | Adherent | RPMI; 10% FBS | RPMI001 | NCI-H23 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 7.87,17.0,-.32,16.7,-.47,-3.2,8.20,-41 | 6.16,5.64,11.9,1.47,1.32,5.46,8.32,9.83 | 8 | Sigmoid | 8.23014535 | 8.00000000 | -40.8638496 | 0.43020 |
| NCIH23_LUNG | 4 | NCIH23 | -2.615303 | FALSE | Adherent | RPMI; 10% FBS | RPMI001 | NCI-H23 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -38,-47,-26,-17,9.44,4.88,-8.0,-1.7 | 19.3,58.9,22.7,9.80,20.5,8.29,14.9,.11 | 8 | Linear | NA | 8.00000000 | -44.1648483 | 1.20230 |
| NCIH23_LUNG | 4 | NCIH23 | -2.615303 | FALSE | Adherent | RPMI; 10% FBS | RPMI001 | NCI-H23 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 7.09,15.0,8.97,11.3,7.07,-4.1,-3.2,-3.7 | .72,9.28,11.9,8.86,2.28,2.57,3.73,10.1 | 8 | Constant | NA | 8.00000000 | 5.4631977 | 0.00000 |
| NCIH322_LUNG | 4 | NCIH322 | -1.244356 | FALSE | Adherent | RPMI;10% FBS; 2%Glutamine | MCCOYS5A001 | NCI-H322 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 6.34,7.33,-3.3,.21,-14,-24,-76,-91 | 4.67,6.12,5.50,.51,11.6,1.02,1.01,1.91 | 8 | Sigmoid | 1.25279534 | 1.36212850 | -91.1652679 | 1.34630 |
| NCIH322_LUNG | 4 | NCIH322 | -1.244356 | FALSE | Adherent | RPMI;10% FBS; 2%Glutamine | MCCOYS5A001 | NCI-H322 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -20,-11,-12,-9.6,1.63,-16,-72,-82 | 22.1,7.09,3.41,12.7,1.74,1.50,.029,.83 | 8 | Sigmoid | 1.48443556 | 1.60569716 | -81.6505203 | 1.46520 |
| NCIH322_LUNG | 4 | NCIH322 | -1.244356 | FALSE | Adherent | RPMI;10% FBS; 2%Glutamine | MCCOYS5A001 | NCI-H322 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 6.24,2.04,.46,-6.7,-22,-66,-76,-86 | 5.78,3.09,4.89,6.13,2.34,9.81,.23,6.07 | 8 | Sigmoid | 0.48323479 | 0.64464504 | -84.2025833 | 1.97460 |
| NCIH441_LUNG | 4 | NCIH441 | -2.228915 | FALSE | Adherent | RPMI; 10% FBS | RPMI001 | NCI-H441 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | .68,2.12,8.83,-2.4,1.78,-21,-3.3,-33 | 4.66,3.48,25.1,2.82,20.7,7.53,10.3,6.00 | 8 | Sigmoid | 8.56892962 | 8.00000000 | -31.0973415 | 0.43460 |
| NCIH441_LUNG | 4 | NCIH441 | -2.228915 | FALSE | Adherent | RPMI; 10% FBS | RPMI001 | NCI-H441 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -11,-5.2,-15,-5.2,-4.7,-9.4,-12,-40 | 8.07,12.9,17.4,3.33,5.80,1.64,7.61,8.54 | 8 | Sigmoid | 6.27163458 | 8.00000000 | -40.1252937 | 0.82770 |
| NCIH441_LUNG | 4 | NCIH441 | -2.228915 | FALSE | Adherent | RPMI; 10% FBS | RPMI001 | NCI-H441 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 14.5,1.35,20.6,9.63,13.5,-.46,-10,-29 | 17.7,.43,7.34,5.11,8.21,1.76,6.97,1.92 | 8 | Sigmoid | 3.36504889 | 8.00000000 | -28.6947022 | 0.30660 |
| NCIH460_LUNG | 2 | NCIH460 | -1.737824 | FALSE | Adherent | RPMI; 10% FBS | NCI-H460 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 3.32,12.7,2.62,6.75,-6.1,-4.4,-2.3,-2.0 | 10.6,15.7,11.2,1.23,10.3,14.8,3.69,6.71 | 8 | Constant | NA | 8.00000000 | 1.4731489 | 0.11020 | |
| NCIH460_LUNG | 2 | NCIH460 | -1.737824 | FALSE | Adherent | RPMI; 10% FBS | NCI-H460 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -15,-3.6,1.54,-21,1.19,-.71,-25,-20 | 1.44,10.4,1.29,5.08,3.95,1.83,2.05,4.01 | 8 | Constant | NA | 8.00000000 | -12.5118055 | 0.87230 | |
| NCIH460_LUNG | 2 | NCIH460 | -1.737824 | FALSE | Adherent | RPMI; 10% FBS | NCI-H460 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | .052,.18,8.84,.73,7.74,-5.0,-2.6,-6.4 | 3.51,1.25,4.97,7.09,2.83,10.8,3.32,3.01 | 8 | Constant | NA | 8.00000000 | 0.2885649 | 0.06994 | |
| NCIH520_LUNG | 3 | NCIH520 | -1.536423 | FALSE | RPMI; 10% FBS | NCI-H520 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.37,7.12,3.38,6.10,1.41,-.95,1.57,-.68 | 11.4,18.3,19.2,15.4,3.21,10.7,14.3,7.15 | 8 | Constant | NA | 8.00000000 | 1.4128480 | 0.00000 | ||
| NCIH520_LUNG | 3 | NCIH520 | -1.536423 | FALSE | RPMI; 10% FBS | NCI-H520 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -12,-14,-4.5,-18,-2.2,-20,-11,-39 | 2.72,9.40,11.0,6.70,3.67,3.07,5.25,11.1 | 8 | Sigmoid | 8.20751625 | 8.00000000 | -38.6709251 | 0.83200 | ||
| NCIH520_LUNG | 3 | NCIH520 | -1.536423 | FALSE | RPMI; 10% FBS | NCI-H520 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.1,.58,2.58,4.28,2.13,12.9,3.81,-1.1 | 5.27,5.64,14.2,11.1,13.6,6.58,24.7,.26 | 8 | Constant | NA | 8.00000000 | 0.7389826 | 0.00000 | ||
| PC14_LUNG | 3 | PC14 | -1.521806 | FALSE | RPMI; 10% FBS | PC-14 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 4.39,1.92,6.76,1.95,-.43,-25,-54,-97 | 3.51,.93,5.27,4.91,3.41,5.81,6.96,.94 | 8 | Sigmoid | 3.44203162 | 2.14965892 | -96.6235428 | 1.62710 | ||
| PC14_LUNG | 3 | PC14 | -1.521806 | FALSE | RPMI; 10% FBS | PC-14 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -22,-13,-12,-22,-55,-75,-82,-82 | 7.20,4.07,3.15,.31,2.29,1.49,1.00,3.83 | 8 | Sigmoid | 0.21271516 | 0.22603795 | -81.4763489 | 3.32910 | ||
| PC14_LUNG | 3 | PC14 | -1.521806 | FALSE | RPMI; 10% FBS | PC-14 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | .95,-17,-31,-60,-67,-75,-78,-91 | 1.57,2.76,2.27,2.23,1.32,.39,.83,.91 | 8 | Sigmoid | 0.01340779 | 0.06461856 | -86.9571457 | 3.71770 | ||
| RERFLCAI_LUNG | 2 | RERFLCAI | -1.207378 | FALSE | Adherent | EMEM: 10% FBS | RERF-LC-AI | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -3.2,-2.6,-5.4,-2.3,-3.1,-4.2,-12,-25 | 4.57,5.48,4.03,4.92,5.02,2.21,1.62,.43 | 8 | Sigmoid | 3.19285536 | 8.00000000 | -25.1049042 | 0.30600 | |
| RERFLCAI_LUNG | 2 | RERFLCAI | -1.207378 | FALSE | Adherent | EMEM: 10% FBS | RERF-LC-AI | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -5.6,-8.5,-1.9,-2.5,.57,-2.3,-9.4,-19 | 1.70,2.28,.36,5.16,9.63,2.31,1.76,3.00 | 8 | Constant | NA | 8.00000000 | -5.8939157 | 0.49540 | |
| RERFLCAI_LUNG | 2 | RERFLCAI | -1.207378 | FALSE | Adherent | EMEM: 10% FBS | RERF-LC-AI | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -1.9,1.68,-6.0,-1.3,-1.1,-2.5,-11,-27 | 2.31,2.84,6.22,3.84,6.82,4.58,4.34,5.00 | 8 | Sigmoid | 3.55668688 | 8.00000000 | -26.9175682 | 0.29990 | |
| SKMES1_LUNG | 2 | SKMES1 | -1.317730 | FALSE | Adherent | DMEM; 10% FBS | SK-MES-1 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 22.4,13.9,27.1,9.43,-11,-37,-61,-92 | .99,10.8,11.2,11.1,1.28,10.8,6.88,2.16 | 8 | Sigmoid | 1.54817939 | 1.47644412 | -90.7866669 | 2.02590 | |
| SKMES1_LUNG | 2 | SKMES1 | -1.317730 | FALSE | Adherent | DMEM; 10% FBS | SK-MES-1 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | -10,9.20,-19,-17,-5.9,-39,-64,-94 | 13.3,15.5,.26,11.2,24.1,11.1,2.83,.97 | 8 | Sigmoid | 5.73222542 | 1.42946458 | -93.8255234 | 2.08880 | |
| SKMES1_LUNG | 2 | SKMES1 | -1.317730 | FALSE | Adherent | DMEM; 10% FBS | SK-MES-1 | lung_NSC | ⋯ | EGFR | .0025,.0080,.025,.080,.25,.80,2.53,8 | 18.0,17.2,3.24,-14,-21,-42,-74,-90 | 11.4,8.40,18.9,17.9,5.17,13.4,2.34,.077 | 8 | Sigmoid | 1.08531237 | 0.96515131 | -91.4273529 | 2.37910 |
In [276]:
drug_res <- select_cl %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`)) %>% filter(cell_line %in% names(score))
head(drug_res)
| cell_line | IC50 (uM) | EC50 (uM) |
|---|---|---|
| <chr> | <dbl> | <dbl> |
| A549_LUNG | 4.227246 | 8.692716 |
| A549_LUNG | 8.000000 | 2.060424 |
| A549_LUNG | 8.000000 | 2.394718 |
| CALU6_LUNG | 8.000000 | NA |
| CALU6_LUNG | 8.000000 | NA |
| CALU6_LUNG | 7.807984 | 8.704103 |
In [277]:
drug_res$score = score[as.character(drug_res$cell_line)]
In [278]:
#cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method = 'pearson')
#cor.test(drug_res$LN_IC50,drug_res$score,method = 'kendall')
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`IC50 (uM)` and drug_res$score
S = 180450, p-value = 0.0003379
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.3462008
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`EC50 (uM)` and drug_res$score
S = 44641, p-value = 0.08214
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.1803435
In [185]:
score <- apply(score_df[,2:32],MARGIN = 2,FUN = FUN5)
In [186]:
drug_res$score = score[as.character(drug_res$cell_line)]
In [187]:
#cor.test(drug_res$LN_IC50,drug_res$score,alternative = 'less',method = 'pearson')
#cor.test(drug_res$LN_IC50,drug_res$score,method = 'kendall')
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`IC50 (uM)` and drug_res$score
S = 156121, p-value = 0.05733
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.1646965
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`EC50 (uM)` and drug_res$score
S = 37114, p-value = 0.5567
alternative hypothesis: true rho is less than 0
sample estimates:
rho
0.01865623
In [ ]:
In [191]:
score_df <- merge(EGFR_drug_exp,drivers_pos,by = 'V1',all = F)
score <- apply(score_df[,2:32],MARGIN = 2,FUN = FUN1)
drug_res <- select_cl %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`)) %>% filter(cell_line %in% names(score))
drug_res$score = score[as.character(drug_res$cell_line)]
In [192]:
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`IC50 (uM)` and drug_res$score
S = 183277, p-value = 0.0001464
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.36729
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`EC50 (uM)` and drug_res$score
S = 42137, p-value = 0.1906
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.1141338
In [193]:
score_df <- merge(EGFR_drug_exp,drivers_neg,by = 'V1',all = F)
score <- apply(score_df[,2:32],MARGIN = 2,FUN = FUN1)
drug_res <- select_cl %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`)) %>% filter(cell_line %in% names(score))
drug_res$score = score[as.character(drug_res$cell_line)]
In [194]:
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`IC50 (uM)` and drug_res$score
S = 178481, p-value = 0.0005852
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.3315071
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`EC50 (uM)` and drug_res$score
S = 48171, p-value = 0.0164
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2737041
pipe¶
read data¶
In [203]:
read_dir <- file.path('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/output_new/')
run_name <- 'main_PC9_LUNG_run10_t0_sen_res'
#GDSC
test_drug <- 'Osimertinib'
cancer_type <- 'LUAD'
#CCLE
CCLE_Target <- 'EGFR'
CCLE_tumor_type <- 'Lung'
#gene <- 'SLC1A5'
read_dir <- file.path(read_dir,run_name)
drivers <- fread(file.path(read_dir,'driver_summary_shap_total.csv'))
drivers$rank_shap_weight = rank(-drivers$weight_shap_total_mean)
drivers$rank_grad_weight = rank(-abs(drivers$weight_grad_total_dir_mean))
drivers$directions = drivers$weight_grad_total_dir_mean >= 0
drivers$directions_cal = as.numeric(drivers$directions)
drivers[!drivers$directions,]$directions_cal = -1
In [204]:
GDSC_exp <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/Cell_line_RMA_proc_basalExp.txt')
GDSC_compounds <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/screened_compounds_rel_8.5.csv')
GDSC_cellline <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/Cell_Lines_Details.xlsx')
GDSC_drug1 <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/GDSC1_fitted_dose_response_27Oct23.xlsx')
GDSC_drug2 <- openxlsx::read.xlsx('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/GDSC/GDSC2_fitted_dose_response_27Oct23.xlsx')
GDSC_drug <- rbind(GDSC_drug1,GDSC_drug2)
In [205]:
GDSC_select <- GDSC_drug %>% dplyr::filter(GDSC_drug$COSMIC_ID %in% substring(colnames(GDSC_exp),6)) %>%
dplyr::filter(DRUG_NAME == test_drug) %>%
dplyr::filter(TCGA_DESC == cancer_type)
#GDSC_select
In [206]:
GDSC_select_exp <- GDSC_exp %>% dplyr::select(c(1,which(substring(colnames(GDSC_exp),6) %in% GDSC_select$COSMIC_ID)))
In [207]:
CCLE_exp <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_DepMap_18Q1_RNAseq_RPKM_20180214.gct')
CCLE_info <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/sample_info.csv')
CCLE_drug <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_NP24.2009_Drug_data_2015.02.24.csv')
CCLE_drug_info <- readxl::read_excel('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/CCLE/CCLE_GNF_data_090613.xls')
In [208]:
#利用aggregate函数,对相同的基因名按列取平均
#CCLE_exp <- aggregate(.~Description,mean,data=CCLE_exp)
CCLE_merge_info <- merge(CCLE_info,CCLE_drug,by.x = 'cell_line',by.y='CCLE Cell Line Name',all = F)
CCLE_select <- CCLE_merge_info %>%
filter(Target == CCLE_Target) %>%
filter(tumor_type == CCLE_tumor_type)
CCLE_select_name <- CCLE_select%>%
dplyr::select('cell_line') %>%
t() %>% c()
CCLE_drug_exp <- CCLE_exp %>%
dplyr::select(c('Description',CCLE_select_name)) %>%
dplyr::filter(Description %in% drivers$V1) %>%
as.data.frame()
test single gene¶
GDSC¶
In [1004]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% GDSC_exp$GENE_SYMBOLS]
for ( gene in gene_list){
message(gene)
GDSC_single_gene <- GDSC_select_exp%>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()
GDSC_single_gene <- GDSC_single_gene %>% mutate(COSMIC_ID = substring(rownames(GDSC_single_gene),6))
GDSC_single_gene <- GDSC_single_gene[2:nrow(GDSC_single_gene),] %>% apply(2,as.numeric)
colnames(GDSC_single_gene) <- c('gene','COSMIC_ID')
GDSC_single_gene <- GDSC_single_gene %>% as.data.frame()
GDSC_single_gene$group <- GDSC_single_gene$gene >= mean(GDSC_single_gene$gene) %>% as.numeric()
cor_df <- GDSC_select %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>%
merge(GDSC_single_gene,by = 'COSMIC_ID',all = T)
pvalue_list <- cor.test(cor_df$LN_IC50,cor_df$gene,alternative = 'two.sided',method = 'pearson')[[3]] %>% c(pvalue_list,.)
corr_list <- cor.test(cor_df$LN_IC50,cor_df$gene,alternative = 'two.sided',method = 'pearson')[[4]] %>% c(corr_list,.)
}
gene_cor_plot_df <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B MSH6 TFDP1 MYO1B TUBA1B SLC1A5 TGM2 BARD1 TUBB4B RRBP1 ANXA2 PLK2 PSMC4 DGKE CEBPD HSPA2 RAB31 TFAP2C CD9 TPM1 LMO7 KLF5 TRIB1 HIST1H1C DAAM1
In [1005]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% GDSC_exp$GENE_SYMBOLS]
for ( gene in gene_list){
message(gene)
GDSC_single_gene <- GDSC_select_exp%>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()
GDSC_single_gene <- GDSC_single_gene %>% mutate(COSMIC_ID = substring(rownames(GDSC_single_gene),6))
GDSC_single_gene <- GDSC_single_gene[2:nrow(GDSC_single_gene),] %>% apply(2,as.numeric)
colnames(GDSC_single_gene) <- c('gene','COSMIC_ID')
GDSC_single_gene <- GDSC_single_gene %>% as.data.frame()
GDSC_single_gene$group <- GDSC_single_gene$gene >= mean(GDSC_single_gene$gene) %>% as.numeric()
cor_df <- GDSC_select %>%
dplyr::select(c('COSMIC_ID','LN_IC50','AUC')) %>%
merge(GDSC_single_gene,by = 'COSMIC_ID',all = T)
pvalue_list <- cor.test(cor_df$AUC,cor_df$gene,alternative = 'two.sided',method = 'pearson')[[3]] %>% c(pvalue_list,.)
corr_list <- cor.test(cor_df$AUC,cor_df$gene,alternative = 'two.sided',method = 'pearson')[[4]] %>% c(corr_list,.)
}
gene_cor_plot_df_auc <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B MSH6 TFDP1 MYO1B TUBA1B SLC1A5 TGM2 BARD1 TUBB4B RRBP1 ANXA2 PLK2 PSMC4 DGKE CEBPD HSPA2 RAB31 TFAP2C CD9 TPM1 LMO7 KLF5 TRIB1 HIST1H1C DAAM1
In [1006]:
gene_cor_plot_df$with = 'IC50'
gene_cor_plot_df_auc$with = 'AUC'
gene_cor_plot_df <- rbind(gene_cor_plot_df,gene_cor_plot_df_auc)
In [1007]:
gene_cor_plot_df$p_status <- gene_cor_plot_df$pvalue <= 0.05
In [1008]:
gene_cor_plot_df
| gene | pvalue | corr | with | p_status |
|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <chr> | <lgl> |
| GTF2B | 9.714737e-01 | 0.004675303 | IC50 | FALSE |
| MSH6 | 7.513098e-01 | -0.041415908 | IC50 | FALSE |
| TFDP1 | 2.885206e-01 | 0.138099376 | IC50 | FALSE |
| MYO1B | 1.299438e-02 | -0.316363859 | IC50 | TRUE |
| TUBA1B | 2.074346e-01 | 0.163705070 | IC50 | FALSE |
| SLC1A5 | 4.410045e-01 | 0.100480841 | IC50 | FALSE |
| TGM2 | 9.086580e-01 | -0.014999345 | IC50 | FALSE |
| BARD1 | 4.543944e-01 | -0.097575263 | IC50 | FALSE |
| TUBB4B | 5.366215e-01 | -0.080657379 | IC50 | FALSE |
| RRBP1 | 2.317320e-01 | -0.155403122 | IC50 | FALSE |
| ANXA2 | 9.750867e-01 | 0.004082954 | IC50 | FALSE |
| PLK2 | 4.505011e-01 | -0.098415166 | IC50 | FALSE |
| PSMC4 | 1.889890e-01 | 0.170478791 | IC50 | FALSE |
| DGKE | 3.739904e-01 | 0.115840427 | IC50 | FALSE |
| CEBPD | 4.153328e-01 | -0.106193334 | IC50 | FALSE |
| HSPA2 | 9.242777e-01 | -0.012426096 | IC50 | FALSE |
| RAB31 | 6.757715e-01 | -0.054640531 | IC50 | FALSE |
| TFAP2C | 2.013561e-01 | -0.165888109 | IC50 | FALSE |
| CD9 | 4.987845e-01 | -0.088261276 | IC50 | FALSE |
| TPM1 | 2.873641e-01 | 0.138427871 | IC50 | FALSE |
| LMO7 | 4.635234e-01 | 0.095621202 | IC50 | FALSE |
| KLF5 | 1.557443e-02 | -0.308475948 | IC50 | TRUE |
| TRIB1 | 9.899296e-01 | 0.001650178 | IC50 | FALSE |
| HIST1H1C | 4.762866e-01 | 0.092923887 | IC50 | FALSE |
| DAAM1 | 1.843256e-05 | -0.518766520 | IC50 | TRUE |
| GTF2B | 9.624117e-01 | 0.006161451 | AUC | FALSE |
| MSH6 | 6.555349e-01 | -0.058272459 | AUC | FALSE |
| TFDP1 | 2.715547e-01 | 0.143008635 | AUC | FALSE |
| MYO1B | 4.529751e-02 | -0.257304347 | AUC | TRUE |
| TUBA1B | 1.858683e-01 | 0.171671061 | AUC | FALSE |
| SLC1A5 | 5.043171e-01 | 0.087131579 | AUC | FALSE |
| TGM2 | 6.776099e-01 | -0.054312656 | AUC | FALSE |
| BARD1 | 2.644980e-01 | -0.145110732 | AUC | FALSE |
| TUBB4B | 4.437621e-01 | -0.099878491 | AUC | FALSE |
| RRBP1 | 1.407588e-01 | -0.190804506 | AUC | FALSE |
| ANXA2 | 7.805462e-01 | 0.036413499 | AUC | FALSE |
| PLK2 | 4.847022e-01 | -0.091166469 | AUC | FALSE |
| PSMC4 | 4.423388e-02 | 0.258544093 | AUC | TRUE |
| DGKE | 5.223808e-01 | 0.083486580 | AUC | FALSE |
| CEBPD | 5.670318e-01 | -0.074735438 | AUC | FALSE |
| HSPA2 | 4.755087e-01 | 0.093087162 | AUC | FALSE |
| RAB31 | 6.615541e-01 | -0.057187687 | AUC | FALSE |
| TFAP2C | 3.154220e-01 | -0.130688857 | AUC | FALSE |
| CD9 | 2.719604e-01 | -0.142888923 | AUC | FALSE |
| TPM1 | 1.554080e-01 | 0.184150134 | AUC | FALSE |
| LMO7 | 5.285555e-01 | 0.082255227 | AUC | FALSE |
| KLF5 | 1.940409e-01 | -0.168578308 | AUC | FALSE |
| TRIB1 | 9.197905e-01 | 0.013164815 | AUC | FALSE |
| HIST1H1C | 2.410690e-01 | 0.152373946 | AUC | FALSE |
| DAAM1 | 1.178342e-03 | -0.405733131 | AUC | TRUE |
In [961]:
p1 <- ggplot(data = gene_cor_plot_df)+
geom_point(aes(x = with,y = gene,size=abs(corr),color = p_status))+
geom_text(aes(x = with,y = gene,label = round(corr,2)),color = 'white')+
scale_size_continuous(range = c(8, 15))+
scale_colour_manual(values = c('TRUE'= "#8C0303", 'FALSE' = "#2A398C"))+
theme_bw()
p1
In [109]:
ggsave(p1,filename = paste0(run_name,'_',test_drug,'_',cancer_type,'_genecor_with_drug.pdf'),height = 10,width =5)
CCLE¶
In [962]:
rn <- CCLE_drug_exp$Description
CCLE_drug_exp <- CCLE_drug_exp[,2:ncol(CCLE_drug_exp)]
rownames(CCLE_drug_exp) <- rn
CCLE_drug_exp <- log2(CCLE_drug_exp)
CCLE_drug_exp$GENE_SYMBOLS = rownames(CCLE_drug_exp)
In [963]:
table(CCLE_select$Compound)
Erlotinib Lapatinib ZD-6474
31 31 31
In [964]:
CCLE_surv <- CCLE_select %>% filter(Compound == 'Erlotinib') %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`))
In [965]:
gene = 'GTF2B'
cor_df <- CCLE_drug_exp %>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()%>%
mutate(cell_line = colnames(CCLE_drug_exp)) %>%
mutate(exp = as.numeric(CCLE_drug_exp[gene,])) %>%
dplyr::select(c('exp','cell_line')) %>%
merge(y =CCLE_surv,by = 'cell_line',all = F)
#GDSC_single_gene <- GDSC_single_gene[2:nrow(GDSC_single_gene),] %>% apply(2,as.numeric)
#colnames(GDSC_single_gene) <- c('gene','COSMIC_ID')
#GDSC_single_gene <- GDSC_single_gene %>% as.data.frame()
pvalue_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method = 'pearson')[[3]]
corr_list <- cor.test(cor_df$`EC50 (uM)`,cor_df$exp,alternative = 'two.sided',method = 'pearson')[[4]]
pvalue_list
corr_list
Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion”
0.929754179624599
cor: -0.0903369786114959
In [966]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% CCLE_drug_exp$GENE_SYMBOLS]
CCLE_surv <- CCLE_select %>% filter(Compound == 'Erlotinib') %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`))
for ( gene in gene_list){
message(gene)
cor_df <- CCLE_drug_exp %>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()%>%
mutate(cell_line = colnames(CCLE_drug_exp)) %>%
mutate(exp = as.numeric(CCLE_drug_exp[gene,])) %>%
dplyr::select(c('exp','cell_line')) %>%
merge(y =CCLE_surv,by = 'cell_line',all = F)
pvalue_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method = 'pearson')[[3]] %>% c(pvalue_list,.)
corr_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method = 'pearson')[[4]] %>% c(corr_list,.)
}
cor_plot_df_ic50 <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” MSH6 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TFDP1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” MYO1B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TUBA1B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” SLC1A5 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TGM2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” BARD1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TUBB4B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” RRBP1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” ANXA2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” PLK2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” PSMC4 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” DGKE Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” CEBPD Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” HSPA2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” RAB31 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TFAP2C Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” CD9 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TPM1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” LMO7 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” KLF5 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TRIB1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” HIST1H1C Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” DAAM1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion”
In [969]:
cor_plot_df_ic50
| gene | pvalue | corr | with |
|---|---|---|---|
| <chr> | <dbl> | <dbl> | <chr> |
| GTF2B | 0.92975418 | -0.01651038 | Erlotinib |
| MSH6 | 0.56196312 | 0.10830159 | Erlotinib |
| TFDP1 | 0.01508307 | -0.43256213 | Erlotinib |
| MYO1B | 0.28797688 | -0.19706211 | Erlotinib |
| TUBA1B | 0.44648053 | 0.14186892 | Erlotinib |
| SLC1A5 | 0.49956199 | 0.12595750 | Erlotinib |
| TGM2 | 0.77647654 | -0.05314013 | Erlotinib |
| BARD1 | 0.39443438 | 0.15849550 | Erlotinib |
| TUBB4B | 0.04517675 | -0.36231141 | Erlotinib |
| RRBP1 | 0.02976071 | -0.39072017 | Erlotinib |
| ANXA2 | 0.04216628 | -0.36716625 | Erlotinib |
| PLK2 | 0.21199387 | -0.23060607 | Erlotinib |
| PSMC4 | 0.48109570 | 0.13138846 | Erlotinib |
| DGKE | 0.81497566 | 0.04380999 | Erlotinib |
| CEBPD | 0.94862159 | 0.01206895 | Erlotinib |
| HSPA2 | 0.39207702 | 0.15927702 | Erlotinib |
| RAB31 | 0.15069316 | 0.26435422 | Erlotinib |
| TFAP2C | 0.29978271 | -0.19239185 | Erlotinib |
| CD9 | 0.12979298 | -0.27811604 | Erlotinib |
| TPM1 | 0.76846900 | 0.05509583 | Erlotinib |
| LMO7 | 0.10374793 | -0.29777327 | Erlotinib |
| KLF5 | 0.26370749 | -0.20706362 | Erlotinib |
| TRIB1 | 0.28921055 | -0.19656845 | Erlotinib |
| HIST1H1C | 0.50095518 | -0.12555192 | Erlotinib |
| DAAM1 | 0.02277635 | -0.40778755 | Erlotinib |
In [970]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% CCLE_drug_exp$GENE_SYMBOLS]
CCLE_surv <- CCLE_select %>% filter(Compound == 'Lapatinib') %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`))
for ( gene in gene_list){
message(gene)
cor_df <- CCLE_drug_exp %>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()%>%
mutate(cell_line = colnames(CCLE_drug_exp)) %>%
mutate(exp = as.numeric(CCLE_drug_exp[gene,])) %>%
dplyr::select(c('exp','cell_line')) %>%
merge(y =CCLE_surv,by = 'cell_line',all = F)
pvalue_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method = 'pearson')[[3]] %>% c(pvalue_list,.)
corr_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method = 'pearson')[[4]] %>% c(corr_list,.)
}
cor_plot_df_ic50_2 <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” MSH6 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TFDP1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” MYO1B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TUBA1B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” SLC1A5 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TGM2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” BARD1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TUBB4B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” RRBP1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” ANXA2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” PLK2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” PSMC4 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” DGKE Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” CEBPD Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” HSPA2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” RAB31 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TFAP2C Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” CD9 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TPM1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” LMO7 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” KLF5 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TRIB1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” HIST1H1C Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” DAAM1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion”
In [967]:
pvalue_list <- c()
corr_list <- c()
gene_list <- drivers$V1[drivers$V1 %in% CCLE_drug_exp$GENE_SYMBOLS]
CCLE_surv <- CCLE_select %>% filter(Compound == 'ZD-6474') %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`))
for ( gene in gene_list){
message(gene)
cor_df <- CCLE_drug_exp %>% dplyr::filter(GENE_SYMBOLS == gene) %>% t() %>% as.data.frame()%>%
mutate(cell_line = colnames(CCLE_drug_exp)) %>%
mutate(exp = as.numeric(CCLE_drug_exp[gene,])) %>%
dplyr::select(c('exp','cell_line')) %>%
merge(y =CCLE_surv,by = 'cell_line',all = F)
pvalue_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method = 'pearson')[[3]] %>% c(pvalue_list,.)
corr_list <- cor.test(cor_df$`IC50 (uM)`,cor_df$exp,alternative = 'two.sided',method = 'pearson')[[4]] %>% c(corr_list,.)
}
cor_plot_df_ic50_3 <- data.frame(gene = gene_list,pvalue= pvalue_list,corr = corr_list)
GTF2B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” MSH6 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TFDP1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” MYO1B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TUBA1B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” SLC1A5 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TGM2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” BARD1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TUBB4B Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” RRBP1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” ANXA2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” PLK2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” PSMC4 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” DGKE Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” CEBPD Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” HSPA2 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” RAB31 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TFAP2C Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” CD9 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TPM1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” LMO7 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” KLF5 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” TRIB1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” HIST1H1C Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion” DAAM1 Warning message: “There was 1 warning in `mutate()`. ℹ In argument: `exp = as.numeric(CCLE_drug_exp[gene, ])`. Caused by warning: ! NAs introduced by coercion”
In [971]:
cor_plot_df_ic50$with = 'Erlotinib'
cor_plot_df_ic50_2$with = 'Lapatinib'
cor_plot_df_ic50_3$with = 'ZD-6474'
cor_plot_df <- rbind(cor_plot_df_ic50,cor_plot_df_ic50_2,cor_plot_df_ic50_3)
In [972]:
cor_plot_df
| gene | pvalue | corr | with |
|---|---|---|---|
| <chr> | <dbl> | <dbl> | <chr> |
| GTF2B | 0.92975418 | -0.01651038 | Erlotinib |
| MSH6 | 0.56196312 | 0.10830159 | Erlotinib |
| TFDP1 | 0.01508307 | -0.43256213 | Erlotinib |
| MYO1B | 0.28797688 | -0.19706211 | Erlotinib |
| TUBA1B | 0.44648053 | 0.14186892 | Erlotinib |
| SLC1A5 | 0.49956199 | 0.12595750 | Erlotinib |
| TGM2 | 0.77647654 | -0.05314013 | Erlotinib |
| BARD1 | 0.39443438 | 0.15849550 | Erlotinib |
| TUBB4B | 0.04517675 | -0.36231141 | Erlotinib |
| RRBP1 | 0.02976071 | -0.39072017 | Erlotinib |
| ANXA2 | 0.04216628 | -0.36716625 | Erlotinib |
| PLK2 | 0.21199387 | -0.23060607 | Erlotinib |
| PSMC4 | 0.48109570 | 0.13138846 | Erlotinib |
| DGKE | 0.81497566 | 0.04380999 | Erlotinib |
| CEBPD | 0.94862159 | 0.01206895 | Erlotinib |
| HSPA2 | 0.39207702 | 0.15927702 | Erlotinib |
| RAB31 | 0.15069316 | 0.26435422 | Erlotinib |
| TFAP2C | 0.29978271 | -0.19239185 | Erlotinib |
| CD9 | 0.12979298 | -0.27811604 | Erlotinib |
| TPM1 | 0.76846900 | 0.05509583 | Erlotinib |
| LMO7 | 0.10374793 | -0.29777327 | Erlotinib |
| KLF5 | 0.26370749 | -0.20706362 | Erlotinib |
| TRIB1 | 0.28921055 | -0.19656845 | Erlotinib |
| HIST1H1C | 0.50095518 | -0.12555192 | Erlotinib |
| DAAM1 | 0.02277635 | -0.40778755 | Erlotinib |
| GTF2B | 0.88778965 | 0.02642410 | Lapatinib |
| MSH6 | 0.65733708 | 0.08294254 | Lapatinib |
| TFDP1 | 0.27349904 | -0.20295966 | Lapatinib |
| MYO1B | 0.08181342 | -0.31746279 | Lapatinib |
| TUBA1B | 0.26284187 | 0.20743117 | Lapatinib |
| ⋮ | ⋮ | ⋮ | ⋮ |
| LMO7 | 0.14937270 | -0.26518112 | Lapatinib |
| KLF5 | 0.05505435 | -0.34800671 | Lapatinib |
| TRIB1 | 0.23498666 | -0.21971159 | Lapatinib |
| HIST1H1C | 0.58040949 | -0.10326080 | Lapatinib |
| DAAM1 | 0.01127564 | -0.44904895 | Lapatinib |
| GTF2B | 0.41813530 | -0.15078139 | ZD-6474 |
| MSH6 | 0.50852074 | 0.12335927 | ZD-6474 |
| TFDP1 | 0.22806822 | -0.22291079 | ZD-6474 |
| MYO1B | 0.57433335 | -0.10491314 | ZD-6474 |
| TUBA1B | 0.63365707 | -0.08908780 | ZD-6474 |
| SLC1A5 | 0.28427088 | 0.19855327 | ZD-6474 |
| TGM2 | 0.24046724 | -0.21722184 | ZD-6474 |
| BARD1 | 0.74869276 | -0.05995084 | ZD-6474 |
| TUBB4B | 0.20902769 | -0.23206919 | ZD-6474 |
| RRBP1 | 0.16022545 | -0.25853461 | ZD-6474 |
| ANXA2 | 0.04054861 | -0.36989069 | ZD-6474 |
| PLK2 | 0.05410956 | -0.34928249 | ZD-6474 |
| PSMC4 | 0.57194067 | -0.10556595 | ZD-6474 |
| DGKE | 0.42530412 | -0.14849669 | ZD-6474 |
| CEBPD | 0.38671226 | -0.16106570 | ZD-6474 |
| HSPA2 | 0.29109490 | -0.19581700 | ZD-6474 |
| RAB31 | 0.55915481 | 0.10907555 | ZD-6474 |
| TFAP2C | 0.09441545 | -0.30572149 | ZD-6474 |
| CD9 | 0.11010731 | -0.29266084 | ZD-6474 |
| TPM1 | 0.15491601 | -0.26174438 | ZD-6474 |
| LMO7 | 0.55124632 | -0.11126481 | ZD-6474 |
| KLF5 | 0.53580016 | -0.11558346 | ZD-6474 |
| TRIB1 | 0.89498519 | 0.02471996 | ZD-6474 |
| HIST1H1C | 0.30020616 | -0.19222652 | ZD-6474 |
| DAAM1 | 0.09969599 | -0.30115493 | ZD-6474 |
In [973]:
cor_plot_df$p_status <- cor_plot_df$pvalue <= 0.05
In [974]:
p2 <- ggplot(data = cor_plot_df)+
geom_point(aes(x = with,y = gene,size=abs(corr),color = p_status))+
geom_text(aes(x = with,y = gene,label = round(corr,2)),color = 'white')+
scale_size_continuous(range = c(8, 15))+
scale_colour_manual(values = c('TRUE'= "#8C0303", 'FALSE' = "#2A398C"))+
theme_bw()
p2
In [1010]:
gene_cor_plot_df <- gene_cor_plot_df %>% filter(with == 'IC50')
gene_cor_plot_df$with = 'Osimertinib'
gene_cor_plot_df <- rbind(cor_plot_df,gene_cor_plot_df)
In [1011]:
p3 <- ggplot(data = gene_cor_plot_df)+
geom_point(aes(x = with,y = gene,size=abs(corr),color = p_status))+
geom_text(aes(x = with,y = gene,label = round(corr,2)),color = 'white')+
scale_size_continuous(range = c(8, 15))+
scale_colour_manual(values = c('TRUE'= "#8C0303", 'FALSE' = "#2A398C"))+
theme_bw()
p3
In [1012]:
table(gene_cor_plot_df$gene)
table(gene_cor_plot_df$with)
ANXA2 BARD1 CD9 CEBPD DAAM1 DGKE GTF2B HIST1H1C
4 4 4 4 4 4 4 4
HSPA2 KLF5 LMO7 MSH6 MYO1B PLK2 PSMC4 RAB31
4 4 4 4 4 4 4 4
RRBP1 SLC1A5 TFAP2C TFDP1 TGM2 TPM1 TRIB1 TUBA1B
4 4 4 4 4 4 4 4
TUBB4B
4
Erlotinib Lapatinib Osimertinib ZD-6474
25 25 25 25
In [1019]:
gene_cor_plot_mtx <- gene_cor_plot_df %>% dplyr::select(c('gene','corr','with')) %>% tidyr::spread(key = 'with',value = 'corr')
rownames(gene_cor_plot_mtx) <- gene_cor_plot_mtx$gene
gene_cor_plot_mtx <- gene_cor_plot_mtx[,2:ncol(gene_cor_plot_mtx)] %>% as.matrix()
In [1023]:
gene_cor_plot_mtx
| Erlotinib | Lapatinib | Osimertinib | ZD-6474 | |
|---|---|---|---|---|
| ANXA2 | -0.36716625 | -0.54191262 | 0.004082954 | -0.36989069 |
| BARD1 | 0.15849550 | 0.18913780 | -0.097575263 | -0.05995084 |
| CD9 | -0.27811604 | -0.37724916 | -0.088261276 | -0.29266084 |
| CEBPD | 0.01206895 | 0.04715317 | -0.106193334 | -0.16106570 |
| DAAM1 | -0.40778755 | -0.44904895 | -0.518766520 | -0.30115493 |
| DGKE | 0.04380999 | -0.01012269 | 0.115840427 | -0.14849669 |
| GTF2B | -0.01651038 | 0.02642410 | 0.004675303 | -0.15078139 |
| HIST1H1C | -0.12555192 | -0.10326080 | 0.092923887 | -0.19222652 |
| HSPA2 | 0.15927702 | 0.16280258 | -0.012426096 | -0.19581700 |
| KLF5 | -0.20706362 | -0.34800671 | -0.308475948 | -0.11558346 |
| LMO7 | -0.29777327 | -0.26518112 | 0.095621202 | -0.11126481 |
| MSH6 | 0.10830159 | 0.08294254 | -0.041415908 | 0.12335927 |
| MYO1B | -0.19706211 | -0.31746279 | -0.316363859 | -0.10491314 |
| PLK2 | -0.23060607 | -0.34227929 | -0.098415166 | -0.34928249 |
| PSMC4 | 0.13138846 | 0.02879852 | 0.170478791 | -0.10556595 |
| RAB31 | 0.26435422 | 0.30693390 | -0.054640531 | 0.10907555 |
| RRBP1 | -0.39072017 | -0.46196864 | -0.155403122 | -0.25853461 |
| SLC1A5 | 0.12595750 | 0.07289740 | 0.100480841 | 0.19855327 |
| TFAP2C | -0.19239185 | -0.30826752 | -0.165888109 | -0.30572149 |
| TFDP1 | -0.43256213 | -0.20295966 | 0.138099376 | -0.22291079 |
| TGM2 | -0.05314013 | -0.15071512 | -0.014999345 | -0.21722184 |
| TPM1 | 0.05509583 | 0.01603657 | 0.138427871 | -0.26174438 |
| TRIB1 | -0.19656845 | -0.21971159 | 0.001650178 | 0.02471996 |
| TUBA1B | 0.14186892 | 0.20743117 | 0.163705070 | -0.08908780 |
| TUBB4B | -0.36231141 | -0.26564096 | -0.080657379 | -0.23206919 |
In [1061]:
gene_cor_p_mtx <- gene_cor_plot_df %>% dplyr::select(c('gene','pvalue','with')) %>% tidyr::spread(key = 'with',value = 'pvalue')
rownames(gene_cor_p_mtx) <- gene_cor_p_mtx$gene
gene_cor_p_mtx <- gene_cor_p_mtx[,2:ncol(gene_cor_p_mtx)] %>% as.matrix()
In [1062]:
gene_cor_p_mtx
| Erlotinib | Lapatinib | Osimertinib | ZD-6474 | |
|---|---|---|---|---|
| ANXA2 | 0.04216628 | 0.001639199 | 9.750867e-01 | 0.04054861 |
| BARD1 | 0.39443438 | 0.308186572 | 4.543944e-01 | 0.74869276 |
| CD9 | 0.12979298 | 0.036427233 | 4.987845e-01 | 0.11010731 |
| CEBPD | 0.94862159 | 0.801126935 | 4.153328e-01 | 0.38671226 |
| DAAM1 | 0.02277635 | 0.011275639 | 1.843256e-05 | 0.09969599 |
| DGKE | 0.81497566 | 0.956898558 | 3.739904e-01 | 0.42530412 |
| GTF2B | 0.92975418 | 0.887789647 | 9.714737e-01 | 0.41813530 |
| HIST1H1C | 0.50095518 | 0.580409490 | 4.762866e-01 | 0.30020616 |
| HSPA2 | 0.39207702 | 0.381543662 | 9.242777e-01 | 0.29109490 |
| KLF5 | 0.26370749 | 0.055054351 | 1.557443e-02 | 0.53580016 |
| LMO7 | 0.10374793 | 0.149372696 | 4.635234e-01 | 0.55124632 |
| MSH6 | 0.56196312 | 0.657337078 | 7.513098e-01 | 0.50852074 |
| MYO1B | 0.28797688 | 0.081813421 | 1.299438e-02 | 0.57433335 |
| PLK2 | 0.21199387 | 0.059457365 | 4.505011e-01 | 0.05410956 |
| PSMC4 | 0.48109570 | 0.877778585 | 1.889890e-01 | 0.57194067 |
| RAB31 | 0.15069316 | 0.093049814 | 6.757715e-01 | 0.55915481 |
| RRBP1 | 0.02976071 | 0.008888952 | 2.317320e-01 | 0.16022545 |
| SLC1A5 | 0.49956199 | 0.696746838 | 4.410045e-01 | 0.28427088 |
| TFAP2C | 0.29978271 | 0.091565046 | 2.013561e-01 | 0.09441545 |
| TFDP1 | 0.01508307 | 0.273499044 | 2.885206e-01 | 0.22806822 |
| TGM2 | 0.77647654 | 0.418342261 | 9.086580e-01 | 0.24046724 |
| TPM1 | 0.76846900 | 0.931765363 | 2.873641e-01 | 0.15491601 |
| TRIB1 | 0.28921055 | 0.234986660 | 9.899296e-01 | 0.89498519 |
| TUBA1B | 0.44648053 | 0.262841871 | 2.074346e-01 | 0.63365707 |
| TUBB4B | 0.04517675 | 0.148642007 | 5.366215e-01 | 0.20902769 |
In [1074]:
library(ComplexHeatmap)
library(circlize)
col_fun = colorRamp2(c(-0.25,0,0.25),c('#D28130','white','#559073'))
cell_fun = function(j, i, x, y, width, height, fill) {
if (gene_cor_p_mtx[i,j] <=0.05){
grid.text(sprintf("%.3f", gene_cor_plot_mtx[i, j]), x, y, gp = gpar(fontsize = 10,col = '#A11715', fontface = "bold"))
}else{
grid.text(sprintf("%.3f", gene_cor_plot_mtx[i, j]), x, y, gp = gpar(fontsize = 10,col = 'black'))
}
}
In [1088]:
p <- Heatmap(gene_cor_plot_mtx,
cluster_rows = T,
#clustering_distance_rows = "pearson",
show_row_dend =F,
cluster_columns = T,
row_names_side = "left",
show_column_dend =F,
row_names_gp = gpar(fontsize = 10),
column_names_gp = gpar(fontsize = 10),
col = col_fun,
cell_fun = cell_fun,
rect_gp = gpar(col = "white", lwd = 2),
column_title = 'Drugs target EGFR',
column_title_gp = gpar(fontsize = 20, fontface = "bold"),
row_title = "Driver genes", row_title_rot = 90,
row_title_gp = gpar(fontsize = 20, fontface = "bold"),)
p
In [1089]:
pdf(file = "/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_EGFR_drugs_heatmap.pdf",width = 8/1.5,height = 12/1.5)
p
dev.off()
png: 2
In [1104]:
tgene_cor_p_mtx <- t(gene_cor_p_mtx)
tgene_cor_plot_mtx <- t(gene_cor_plot_mtx)
In [1160]:
drivers <- drivers %>% as.data.frame()
rownames(drivers) <- drivers$V1
directions_cal <- drivers%>% dplyr::select('directions_cal')
weight_cal <- drivers%>% dplyr::select('weight_shap_total_mean') %>% normalize()
In [1179]:
col_fun = colorRamp2(c(-0.25,0,0.25),c('#D28130','white','#559073'))
cell_fun = function(j, i, x, y, width, height, fill) {
if (tgene_cor_p_mtx[i,j] <=0.05){
grid.text(sprintf("%.3f", tgene_cor_plot_mtx[i, j]), x, y, gp = gpar(fontsize = 10,col = '#A11715', fontface = "bold"))
}else{
grid.text(sprintf("%.3f", tgene_cor_plot_mtx[i, j]), x, y, gp = gpar(fontsize = 10,col = 'black'))
}
}
col = list(Direction = c("-1" = "#FF09A4", "1" = "#32510A"))
#library(circlize)
#col_fun2 = colorRamp2(c(-2, -1, 0,1), c("#A6335D", "#ED6193", "#C6C6C6","#91374B"))
ha = HeatmapAnnotation(Direction = as.character(directions_cal[colnames(tgene_cor_plot_mtx),]),
`Normalized SHAP weight` = anno_barplot(weight_cal[colnames(tgene_cor_plot_mtx),],gp = gpar(fill ='#779CD2') ),
col = col,
gp = gpar(col = "white",lty=1,
lwd = 6),
height = unit(25, "mm"),
gap = unit(c(1.5), "mm"))
p <- Heatmap(tgene_cor_plot_mtx,
cluster_rows = T,
#clustering_distance_rows = "pearson",
show_row_dend =F,
cluster_columns = T,
row_names_side = "left",
show_column_dend =F,
row_names_gp = gpar(fontsize = 10),
column_names_gp = gpar(fontsize = 10),
col = col_fun,
cell_fun = cell_fun,
rect_gp = gpar(col = "white", lwd = 2),
column_title = 'Drugs target EGFR',
column_title_gp = gpar(fontsize = 20, fontface = "bold"),
row_title = "Driver genes", row_title_rot = 90,
row_title_gp = gpar(fontsize = 20, fontface = "bold"),
bottom_annotation = ha,)
p
In [1181]:
pdf(file = "/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/H_sen_res_EGFR_drugs_heatmap.pdf",width = 21/1.5,height = 7/1.5)
p
dev.off()
png: 2
test sorce¶
GDSC¶
In [198]:
FUN1 <- function(x){
sum(x*score_df$weight_shap_total_mean)
#sum(x)
}
normalize <- function(v) {
(v - min(v)) / (max(v) - min(v))
}
FUN2 <- function(x){
sum(x*normalize(score_df$weight_shap_total_mean))
#sum(x)
}
FUN3 <- function(x){
mean(x*normalize(score_df$weight_shap_total_mean))
#sum(x)
}
FUN4 <- function(x){
mean(x*score_df$weight_shap_total_mean)
#sum(x)
}
FUN5 <- function(x){
sum(x*score_df$weight_shap_total_mean*score_df$directions_cal)
#sum(x)
}
In [200]:
score_df <- merge(GDSC_select_exp,drivers,by.x = 'GENE_SYMBOLS',by.y = 'V1',all = F)
score <- apply(score_df[,2:ncol(GDSC_select_exp)],MARGIN = 2,FUN = FUN5)
names(score) <- names(score) %>% substring(.,6)
GDSC_select$score = score[as.character(GDSC_select$COSMIC_ID)]
In [201]:
cor.test(GDSC_select$LN_IC50,GDSC_select$score,alternative = 'less',method = 'spearman')
cor.test(GDSC_select$AUC,GDSC_select$score,alternative = 'less',method = 'spearman')
Spearman's rank correlation rho
data: GDSC_select$LN_IC50 and GDSC_select$score
S = 49784, p-value = 0.00666
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.3163406
Spearman's rank correlation rho
data: GDSC_select$AUC and GDSC_select$score
S = 48350, p-value = 0.01506
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.2784241
In [18]:
ggplot(data = GDSC_select,aes(x=score,y=LN_IC50))+
geom_point(size=3,shape=21,color='black',fill='#03658C')+
geom_smooth(size=1.5,method=lm , color="#BA002B", fill="#69b3a2", se=TRUE) +
geom_text(aes(x=0,y=5,label='Cor = 0.3163406 \n P-value = 0.00666'),size=4,color = '#03658C')+
xlab('Drivers causal score')+
ylab('IC50 (uM)')+
theme_classic()
Warning message: “Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. ℹ Please use `linewidth` instead.” Warning message in geom_text(aes(x = 0, y = 5, label = "Cor = 0.3163406 \n P-value = 0.00666"), : “All aesthetics have length 1, but the data has 61 rows. ℹ Did you mean to use `annotate()`?” `geom_smooth()` using formula = 'y ~ x'
In [19]:
corp <- ggplot(data = GDSC_select,aes(x=score,y=LN_IC50))+
geom_point(size=10,shape=21,stork=2,color='black',fill='#559073CC')+
geom_smooth(size=3,method=lm , color="#BA002B", fill="#69b3a2", se=TRUE) +
geom_text(aes(x=-0.005,y=5,label='Cor = 0.3163406 \n P-value = 0.00666'),size=12,color = 'black')+
xlab('Drivers causal score')+
ylab('IC50 (uM)')+
theme_classic()+
#scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=24),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=20,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top'
)
corp
Warning message in geom_point(size = 10, shape = 21, stork = 2, color = "black", :
“Ignoring unknown parameters: `stork`”
Warning message in geom_text(aes(x = -0.005, y = 5, label = "Cor = 0.3163406 \n P-value = 0.00666"), :
“All aesthetics have length 1, but the data has 61 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
In [20]:
ggsave(plot = corp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_corp.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = corp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_corp.png',
width =16/1.5, height =16/1.5)
Warning message in geom_text(aes(x = -0.005, y = 5, label = "Cor = 0.3163406 \n P-value = 0.00666"), : “All aesthetics have length 1, but the data has 61 rows. ℹ Did you mean to use `annotate()`?” `geom_smooth()` using formula = 'y ~ x' Warning message in geom_text(aes(x = -0.005, y = 5, label = "Cor = 0.3163406 \n P-value = 0.00666"), : “All aesthetics have length 1, but the data has 61 rows. ℹ Did you mean to use `annotate()`?” `geom_smooth()` using formula = 'y ~ x'
In [1205]:
# 计算分位数,这里我们分为三组,所以使用0%, 33%, 67% 和 100%的分位数
quantiles <- quantile(GDSC_select$score, probs = c(0, 1/3, 2/3, 1))
# 使用cut函数将score分组,并创建新列group
GDSC_select$group <- cut(GDSC_select$score, breaks = quantiles,
labels = c("Low", "Medium", "High"), include.lowest = TRUE)
GDSC_select$group <- factor(GDSC_select$group,levels = c("High","Medium","Low"))
# 查看结果
head(GDSC_select)
| DATASET | NLME_RESULT_ID | NLME_CURVE_ID | COSMIC_ID | CELL_LINE_NAME | SANGER_MODEL_ID | TCGA_DESC | DRUG_ID | DRUG_NAME | PUTATIVE_TARGET | ⋯ | COMPANY_ID | WEBRELEASE | MIN_CONC | MAX_CONC | LN_IC50 | AUC | RMSE | Z_SCORE | score | group | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <chr> | ⋯ | <dbl> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <fct> | |
| 1 | GDSC2 | 343 | 15952962 | 687777 | Calu-3 | SIDM00922 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | 1046 | Y | 0.001001 | 1 | 0.105057 | 0.827993 | 0.098212 | -1.211272 | 0.006858576 | High |
| 2 | GDSC2 | 343 | 15953984 | 687798 | NCI-H1623 | SIDM00747 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | 1046 | Y | 0.001001 | 1 | 0.196595 | 0.868151 | 0.052729 | -1.139565 | -0.004671004 | Low |
| 3 | GDSC2 | 343 | 15954155 | 687799 | NCI-H1648 | SIDM00746 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | 1046 | Y | 0.001001 | 1 | -0.701592 | 0.800871 | 0.093298 | -1.843171 | 0.005754738 | High |
| 4 | GDSC2 | 343 | 15954434 | 687800 | NCI-H1650 | SIDM00745 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | 1046 | Y | 0.001001 | 1 | 0.407872 | 0.844857 | 0.134400 | -0.974058 | 0.001806272 | Medium |
| 5 | GDSC2 | 343 | 15954715 | 687802 | NCI-H1693 | SIDM00742 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | 1046 | Y | 0.001001 | 1 | 1.111300 | 0.861017 | 0.104960 | -0.423019 | -0.001409169 | Medium |
| 6 | GDSC2 | 343 | 15955064 | 687807 | NCI-H1838 | SIDM00769 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | 1046 | Y | 0.001001 | 1 | 3.462617 | 0.960185 | 0.020880 | 1.418915 | -0.008347880 | Low |
In [1206]:
p <- ggplot(data = GDSC_select,aes(x=group,y=LN_IC50,color=group))+
geom_boxplot(size=1.5)+
ggbeeswarm::geom_quasirandom(method = "smiley",size=6,alpha=0.7)+
theme_classic()+#设置两轴白底主体
theme(line = element_line(size=1.5),#坐标轴除夕及文字大小
axis.title = element_text(size=18),
axis.text = element_text(size=14),
axis.ticks = element_line(size=1.5),
axis.ticks.length = unit(0.3, "cm"))+
ggsci::scale_color_npg()
p
In [1207]:
# 比较Low组与Medium组
low_medium_t_test <- t.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less', subset = group %in% c("Low", "Medium"))
print("Low vs Medium:")
print(low_medium_t_test)
# 比较Low组与High组
low_high_t_test <- t.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less', subset = group %in% c("Low", "High"))
print("Low vs High:")
print(low_high_t_test)
# 比较Medium组与High组
medium_high_t_test <- t.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less', subset = group %in% c("Medium", "High"))
print("Medium vs High:")
print(medium_high_t_test)
[1] "Low vs Medium:"
Welch Two Sample t-test
data: LN_IC50 by group
t = -2.2825, df = 28.817, p-value = 0.01502
alternative hypothesis: true difference in means between group Medium and group Low is less than 0
95 percent confidence interval:
-Inf -0.315564
sample estimates:
mean in group Medium mean in group Low
1.084215 2.319713
[1] "Low vs High:"
Welch Two Sample t-test
data: LN_IC50 by group
t = -2.4293, df = 28.482, p-value = 0.01084
alternative hypothesis: true difference in means between group High and group Low is less than 0
95 percent confidence interval:
-Inf -0.4008829
sample estimates:
mean in group High mean in group Low
0.9841209 2.3197129
[1] "Medium vs High:"
Welch Two Sample t-test
data: LN_IC50 by group
t = -0.14618, df = 37.985, p-value = 0.4423
alternative hypothesis: true difference in means between group High and group Medium is less than 0
95 percent confidence interval:
-Inf 1.054361
sample estimates:
mean in group High mean in group Medium
0.9841209 1.0842150
In [1208]:
# 比较Low组与Medium组
low_medium_t_test <- wilcox.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less', subset = group %in% c("Low", "Medium"))
print("Low vs Medium:")
print(low_medium_t_test)
# 比较Low组与High组
low_high_t_test <- wilcox.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less', subset = group %in% c("Low", "High"))
print("Low vs High:")
print(low_high_t_test)
# 比较Medium组与High组
medium_high_t_test <- wilcox.test(LN_IC50 ~ group, data = GDSC_select,alternative ='less', subset = group %in% c("Medium", "High"))
print("Medium vs High:")
print(medium_high_t_test)
[1] "Low vs Medium:" Wilcoxon rank sum exact test data: LN_IC50 by group W = 135, p-value = 0.02565 alternative hypothesis: true location shift is less than 0 [1] "Low vs High:" Wilcoxon rank sum exact test data: LN_IC50 by group W = 125, p-value = 0.0132 alternative hypothesis: true location shift is less than 0 [1] "Medium vs High:" Wilcoxon rank sum exact test data: LN_IC50 by group W = 195, p-value = 0.4521 alternative hypothesis: true location shift is less than 0
In [1209]:
p <- ggplot(data = GDSC_select,aes(x=group,y=LN_IC50))+
geom_boxplot(aes(fill=group),size=1.5)+
#ggbeeswarm::geom_quasirandom(aes(color=group),method = "smiley",size=6,alpha=0.7)+
ggsignif::geom_signif(aes(x=group,y=LN_IC50),comparisons = list(c('Low', "Medium"),c('Low', "High"),c('Medium', "High")),step_increase = 0.15,map_signif_level = F,test = 't.test',test.args = c('greater')) +
theme_classic()+#设置两轴白底主体
theme(line = element_line(size=1.5),#坐标轴除夕及文字大小
axis.title = element_text(size=18),
axis.text = element_text(size=14),
axis.ticks = element_line(size=1.5),
axis.ticks.length = unit(0.3, "cm"))+
#ggsci::scale_color_npg()+
ggsci::scale_fill_npg()
p
In [1220]:
# 计算分位数,这里我们分为三组,所以使用0%, 33%, 67% 和 100%的分位数
quantiles <- quantile(GDSC_select$score, probs = c(0, 0.5, 1))
# 使用cut函数将score分组,并创建新列group
GDSC_select$group2 <- cut(GDSC_select$score, breaks = quantiles,
labels = c("Low", "High"), include.lowest = TRUE)
GDSC_select$group2 <- factor(GDSC_select$group2,levels = c("High","Low"))
# 查看结果
head(GDSC_select)
| DATASET | NLME_RESULT_ID | NLME_CURVE_ID | COSMIC_ID | CELL_LINE_NAME | SANGER_MODEL_ID | TCGA_DESC | DRUG_ID | DRUG_NAME | PUTATIVE_TARGET | ⋯ | WEBRELEASE | MIN_CONC | MAX_CONC | LN_IC50 | AUC | RMSE | Z_SCORE | score | group | group2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <chr> | <chr> | <chr> | <dbl> | <chr> | <chr> | ⋯ | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <fct> | <fct> | |
| 1 | GDSC2 | 343 | 15952962 | 687777 | Calu-3 | SIDM00922 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | Y | 0.001001 | 1 | 0.105057 | 0.827993 | 0.098212 | -1.211272 | 0.006858576 | High | High |
| 2 | GDSC2 | 343 | 15953984 | 687798 | NCI-H1623 | SIDM00747 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | Y | 0.001001 | 1 | 0.196595 | 0.868151 | 0.052729 | -1.139565 | -0.004671004 | Low | Low |
| 3 | GDSC2 | 343 | 15954155 | 687799 | NCI-H1648 | SIDM00746 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | Y | 0.001001 | 1 | -0.701592 | 0.800871 | 0.093298 | -1.843171 | 0.005754738 | High | High |
| 4 | GDSC2 | 343 | 15954434 | 687800 | NCI-H1650 | SIDM00745 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | Y | 0.001001 | 1 | 0.407872 | 0.844857 | 0.134400 | -0.974058 | 0.001806272 | Medium | High |
| 5 | GDSC2 | 343 | 15954715 | 687802 | NCI-H1693 | SIDM00742 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | Y | 0.001001 | 1 | 1.111300 | 0.861017 | 0.104960 | -0.423019 | -0.001409169 | Medium | Low |
| 6 | GDSC2 | 343 | 15955064 | 687807 | NCI-H1838 | SIDM00769 | LUAD | 1919 | Osimertinib | EGFR | ⋯ | Y | 0.001001 | 1 | 3.462617 | 0.960185 | 0.020880 | 1.418915 | -0.008347880 | Low | Low |
In [1221]:
p <- ggplot(data = GDSC_select,aes(x=group2,y=LN_IC50))+
geom_boxplot(aes(fill=group2),size=1.5)+
#ggbeeswarm::geom_quasirandom(aes(color=group),method = "smiley",size=6,alpha=0.7)+
ggsignif::geom_signif(aes(x=group2,y=LN_IC50),comparisons = list(c('Low', "High")),map_signif_level = F,test = 't.test',test.args = c('greater')) +
theme_classic()+#设置两轴白底主体
theme(line = element_line(size=1.5),#坐标轴除夕及文字大小
axis.title = element_text(size=18),
axis.text = element_text(size=14),
axis.ticks = element_line(size=1.5),
axis.ticks.length = unit(0.3, "cm"))+
#ggsci::scale_color_npg()+
ggsci::scale_fill_npg()
p
In [1247]:
score_boxp <- ggplot(data = GDSC_select,aes(x=group2,y=LN_IC50,fill=group2))+
stat_boxplot(geom = "errorbar",linewidth=1.5,width = 0.5)+
#geom_violin(outliers = F,linewidth=1.5,color='black')+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
#geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
#ggbeeswarm::geom_quasirandom(method = "smiley",size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
comparisons = list(c('Low', "High")),
step_increase = 0.15,
annotations= c('P-value = 0.021'),
textsize = 8,size=1.5,vjust=0) +
theme_classic()+
xlab('Group by CRS')+
ylab('IC50 (uM)')+
ggtitle("GDSC")+
theme_bw()+
#scale_fill_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
ggsci::scale_fill_npg()+
ggsci::scale_color_npg()+
#scale_color_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=28,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'none')
score_boxp
In [1248]:
ggsave(plot = score_boxp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_score_boxp.pdf',
width =14/1.5, height =14/1.5)
ggsave(plot = score_boxp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_score_boxp.png',
width =16/1.5, height =16/1.5)
CCLE¶
In [209]:
rn <- CCLE_drug_exp$Description
CCLE_drug_exp <- CCLE_drug_exp[,2:ncol(CCLE_drug_exp)]
rownames(CCLE_drug_exp) <- rn
CCLE_drug_exp <- log2(CCLE_drug_exp)
CCLE_drug_exp$V1 = rownames(CCLE_drug_exp)
score_df <- merge(CCLE_drug_exp,drivers,by = 'V1',all = F)
score <- apply(score_df[,2:32],MARGIN = 2,FUN = FUN1)
In [210]:
drug_res <- CCLE_select %>%
dplyr::select(c('cell_line',`IC50 (uM)`,`EC50 (uM)`)) %>% filter(cell_line %in% names(score))
drug_res$score = score[as.character(drug_res$cell_line)]
In [211]:
cor.test(drug_res$`IC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
cor.test(drug_res$`EC50 (uM)`,drug_res$score,alternative = 'less',method = 'spearman')
Warning message in cor.test.default(drug_res$`IC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`IC50 (uM)` and drug_res$score
S = 180450, p-value = 0.0003379
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.3462008
Warning message in cor.test.default(drug_res$`EC50 (uM)`, drug_res$score, alternative = "less", : “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: drug_res$`EC50 (uM)` and drug_res$score
S = 44641, p-value = 0.08214
alternative hypothesis: true rho is less than 0
sample estimates:
rho
-0.1803435
In [212]:
ggplot(data = drug_res,aes(x=score,y=`IC50 (uM)`))+
geom_point(size=3,shape=21,color='black',fill='#03658C')+
geom_smooth(size=1.5,method=lm , color="#BA002B", fill="#69b3a2", se=TRUE) +
#geom_text(aes(x=0,y=5,label='Cor = 0.3163406 \n P-value = 0.00666'),size=4,color = '#03658C')+
xlab('Drivers causal score')+
ylab('IC50 (uM)')+
theme_classic()
Warning message:
“Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.”
`geom_smooth()` using formula = 'y ~ x'
In [281]:
corp <- ggplot(data = drug_res,aes(x=score,y=`IC50 (uM)`))+
geom_point(size=10,shape=21,stork=2,color='black',fill='#559073CC')+
geom_smooth(size=3,method=lm , color="#BA002B", fill="#69b3a2", se=TRUE) +
geom_text(aes(x=0.12,y=11,label='Cor = -0.3462 \n P-value = 0.0003379'),size=12,color = 'black')+
xlab('Drivers causal score')+
ylab('IC50 (uM)')+
theme_classic()+
#scale_color_manual(values = c('In two pathway'= '#559073FF','Out of two pathway'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=24),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=20,colour = 'black'),
axis.text.x = element_text(angle = 60,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=24,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'top'
)
corp
Warning message in geom_point(size = 10, shape = 21, stork = 2, color = "black", :
“Ignoring unknown parameters: `stork`”
Warning message in geom_text(aes(x = 0.12, y = 11, label = "Cor = -0.3462 \n P-value = 0.0003379"), :
“All aesthetics have length 1, but the data has 93 rows.
ℹ Did you mean to use `annotate()`?”
`geom_smooth()` using formula = 'y ~ x'
In [282]:
ggsave(plot = corp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_corpCCLE.pdf',
width =16/1.5, height =16/1.5)
ggsave(plot = corp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_corpCCLE.png',
width =16/1.5, height =16/1.5)
Warning message in geom_text(aes(x = 0.12, y = 11, label = "Cor = -0.3462 \n P-value = 0.0003379"), : “All aesthetics have length 1, but the data has 93 rows. ℹ Did you mean to use `annotate()`?” `geom_smooth()` using formula = 'y ~ x' Warning message in geom_text(aes(x = 0.12, y = 11, label = "Cor = -0.3462 \n P-value = 0.0003379"), : “All aesthetics have length 1, but the data has 93 rows. ℹ Did you mean to use `annotate()`?” `geom_smooth()` using formula = 'y ~ x'
In [ ]:
In [1243]:
# 计算分位数,这里我们分为三组,所以使用0%, 33%, 67% 和 100%的分位数
#quantiles <- quantile(drug_res$score, probs = c(0, 1/3, 2/3, 1))
# 使用cut函数将score分组,并创建新列group
drug_res$group <- cut(drug_res$`IC50 (uM)`, breaks = c(-Inf,8,Inf),
labels = c("Sensitive", "Resistance"), include.lowest = TRUE,right=FALSE)
drug_res$group <- factor(drug_res$group,levels = c("Resistance","Sensitive"))
# 查看结果
head(drug_res)
| cell_line | IC50 (uM) | EC50 (uM) | score | group |
|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <fct> |
| A549_LUNG | 4.227246 | 8.692716 | 0.1348375 | Sensitive |
| A549_LUNG | 8.000000 | 2.060424 | 0.1348375 | Resistance |
| A549_LUNG | 8.000000 | 2.394718 | 0.1348375 | Resistance |
| CALU6_LUNG | 8.000000 | NA | 0.1208364 | Resistance |
| CALU6_LUNG | 8.000000 | NA | 0.1208364 | Resistance |
| CALU6_LUNG | 7.807984 | 8.704103 | 0.1208364 | Sensitive |
In [1244]:
p <- ggplot(data = drug_res,aes(x=group,y=score))+
geom_boxplot(aes(fill=group),size=1.5)+
#ggbeeswarm::geom_quasirandom(aes(color=group),method = "smiley",size=6,alpha=0.7)+
ggsignif::geom_signif(aes(x=group,y=score),comparisons = list(c('Sensitive', "Resistance")),map_signif_level = F,test = 't.test',test.args = c('greater')) +
theme_classic()+#设置两轴白底主体
theme(line = element_line(size=1.5),#坐标轴除夕及文字大小
axis.title = element_text(size=18),
axis.text = element_text(size=14),
axis.ticks = element_line(size=1.5),
axis.ticks.length = unit(0.3, "cm"))+
#ggsci::scale_color_npg()+
ggsci::scale_fill_npg()
p
In [1253]:
response_boxp <- ggplot(data = drug_res,aes(x=group,y=score,fill=group))+
stat_boxplot(geom = "errorbar",linewidth=1.5,width = 0.5)+
#geom_violin(outliers = F,linewidth=1.5,color='black')+
geom_boxplot(outliers = F,linewidth=1.5,color='black')+
#geom_jitter(width = 0.15,size=8,shape=21,stroke=1.5,alpha=0.8)+
#ggbeeswarm::geom_quasirandom(method = "smiley",size=8,shape=21,stroke=1.5,alpha=0.8)+
ggsignif::geom_signif(
comparisons = list(c('Sensitive', "Resistance")),
step_increase = 0.15,
annotations= c('P-value = 5.1e-05'),
textsize = 8,size=1.5,vjust=0) +
theme_classic()+
xlab('Drug response')+
ylab('CRS')+
ggtitle("CCLE")+
theme_bw()+
#scale_fill_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
ggsci::scale_fill_npg()+
ggsci::scale_color_npg()+
#scale_color_manual(values = c('T14 T0'= '#559073FF','T14 T7'='#D28130FF'))+
theme(axis.title = element_text(face = 'bold',size=20),
axis.title.x = element_text(vjust = -5),
axis.title.y = element_text(vjust = 6),
axis.text = element_text(face = 'italic',size=18,colour = 'black'),
axis.text.x = element_text(angle = 0,vjust = 0.5),
#axis.text.y = element_text(hjust = 8),
axis.ticks = element_line(linewidth = 1.5),
axis.ticks.length = unit(10,'points'),
axis.line = element_line(linewidth = 1.5),
plot.title = element_text(face = 'bold',size=28,hjust = 0.5,vjust = 8),
plot.margin = margin(50,50,50,50),
panel.border = element_blank(),
panel.grid = element_blank(),
#panel.grid.major.x =element_line(),
legend.key.size = unit(20,'points'),
legend.text = element_text(face = 'italic',size=18),
#legend.title = element_text(face = 'bold',size=18),
legend.title = element_blank(),
legend.position = 'none')
response_boxp
In [1254]:
ggsave(plot = response_boxp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_response_boxp.pdf',
width =14/1.5, height =14/1.5)
ggsave(plot = response_boxp,filename = '/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/sen_res_response_boxp.png',
width =16/1.5, height =16/1.5)
In [ ]:
In [330]:
drug_res
| cell_line | IC50 (uM) | EC50 (uM) | score | group |
|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <fct> |
| A549_LUNG | 4.22724628 | 8.69271626 | 0.1348375 | Sensitive |
| A549_LUNG | 8.00000000 | 2.06042409 | 0.1348375 | Resistance |
| A549_LUNG | 8.00000000 | 2.39471769 | 0.1348375 | Resistance |
| CALU6_LUNG | 8.00000000 | NA | 0.1208364 | Resistance |
| CALU6_LUNG | 8.00000000 | NA | 0.1208364 | Resistance |
| CALU6_LUNG | 7.80798435 | 8.70410325 | 0.1208364 | Sensitive |
| HARA_LUNG | 6.65674067 | 8.86180042 | 0.1230818 | Sensitive |
| HARA_LUNG | 8.00000000 | 2.15157342 | 0.1230818 | Resistance |
| HARA_LUNG | 8.00000000 | 1.00138474 | 0.1230818 | Resistance |
| HCC15_LUNG | 8.00000000 | NA | 0.1307040 | Resistance |
| HCC15_LUNG | 8.00000000 | 2.19705582 | 0.1307040 | Resistance |
| HCC15_LUNG | 8.00000000 | NA | 0.1307040 | Resistance |
| HCC827_LUNG | 0.37213399 | NA | 0.1293782 | Sensitive |
| HCC827_LUNG | 0.31740648 | 0.30715630 | 0.1293782 | Sensitive |
| HCC827_LUNG | 0.03891792 | 0.02933497 | 0.1293782 | Sensitive |
| KNS62_LUNG | 8.00000000 | 3.77516007 | 0.1167083 | Resistance |
| KNS62_LUNG | 8.00000000 | NA | 0.1167083 | Resistance |
| KNS62_LUNG | 8.00000000 | NA | 0.1167083 | Resistance |
| LCLC103H_LUNG | 8.00000000 | NA | 0.1243597 | Resistance |
| LCLC103H_LUNG | 8.00000000 | NA | 0.1243597 | Resistance |
| LCLC103H_LUNG | 8.00000000 | NA | 0.1243597 | Resistance |
| LUDLU1_LUNG | 2.31964210 | NA | 0.1294483 | Sensitive |
| LUDLU1_LUNG | 1.82598734 | 1.01615298 | 0.1294483 | Sensitive |
| LUDLU1_LUNG | 1.65249217 | 0.33664727 | 0.1294483 | Sensitive |
| MORCPR_LUNG | 8.00000000 | 1.09860432 | 0.1189764 | Resistance |
| MORCPR_LUNG | 8.00000000 | NA | 0.1189764 | Resistance |
| MORCPR_LUNG | 8.00000000 | NA | 0.1189764 | Resistance |
| NCIH1299_LUNG | 8.00000000 | 0.61638433 | 0.1219884 | Resistance |
| NCIH1299_LUNG | 8.00000000 | NA | 0.1219884 | Resistance |
| NCIH1299_LUNG | 8.00000000 | 8.62741137 | 0.1219884 | Resistance |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| NCIH2170_LUNG | 0.30098090 | 0.22801438 | 0.12365955 | Sensitive |
| NCIH2170_LUNG | 2.65564489 | 2.74079251 | 0.12365955 | Sensitive |
| NCIH2170_LUNG | 1.56435514 | 1.18077564 | 0.12365955 | Sensitive |
| NCIH2172_LUNG | 8.00000000 | NA | 0.12834069 | Resistance |
| NCIH2172_LUNG | 8.00000000 | NA | 0.12834069 | Resistance |
| NCIH2172_LUNG | 8.00000000 | NA | 0.12834069 | Resistance |
| NCIH23_LUNG | 8.00000000 | 8.23014535 | 0.12581737 | Resistance |
| NCIH23_LUNG | 8.00000000 | NA | 0.12581737 | Resistance |
| NCIH23_LUNG | 8.00000000 | NA | 0.12581737 | Resistance |
| NCIH322_LUNG | 1.36212850 | 1.25279534 | 0.12767091 | Sensitive |
| NCIH322_LUNG | 1.60569716 | 1.48443556 | 0.12767091 | Sensitive |
| NCIH322_LUNG | 0.64464504 | 0.48323479 | 0.12767091 | Sensitive |
| NCIH441_LUNG | 8.00000000 | 8.56892962 | 0.13216528 | Resistance |
| NCIH441_LUNG | 8.00000000 | 6.27163458 | 0.13216528 | Resistance |
| NCIH441_LUNG | 8.00000000 | 3.36504889 | 0.13216528 | Resistance |
| NCIH460_LUNG | 8.00000000 | NA | 0.10931684 | Resistance |
| NCIH460_LUNG | 8.00000000 | NA | 0.10931684 | Resistance |
| NCIH460_LUNG | 8.00000000 | NA | 0.10931684 | Resistance |
| NCIH520_LUNG | 8.00000000 | NA | 0.09788091 | Resistance |
| NCIH520_LUNG | 8.00000000 | 8.20751625 | 0.09788091 | Resistance |
| NCIH520_LUNG | 8.00000000 | NA | 0.09788091 | Resistance |
| PC14_LUNG | 2.14965892 | 3.44203162 | 0.13793057 | Sensitive |
| PC14_LUNG | 0.22603795 | 0.21271516 | 0.13793057 | Sensitive |
| PC14_LUNG | 0.06461856 | 0.01340779 | 0.13793057 | Sensitive |
| RERFLCAI_LUNG | 8.00000000 | 3.19285536 | 0.12752277 | Resistance |
| RERFLCAI_LUNG | 8.00000000 | NA | 0.12752277 | Resistance |
| RERFLCAI_LUNG | 8.00000000 | 3.55668688 | 0.12752277 | Resistance |
| SKMES1_LUNG | 1.47644412 | 1.54817939 | 0.13454178 | Sensitive |
| SKMES1_LUNG | 1.42946458 | 5.73222542 | 0.13454178 | Sensitive |
| SKMES1_LUNG | 0.96515131 | 1.08531237 | 0.13454178 | Sensitive |
add chord plot¶
In [248]:
chord_data <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/res_orgin_state_transition_adata_meta.csv')
In [249]:
chord_data <- chord_data %>% dplyr::select(c('condition','metacell_label2'))
In [250]:
# Transform input data in a adjacency matrix
adjacencyData <- with(chord_data, table(condition, metacell_label2))
In [251]:
adjacencyData
metacell_label2
condition 0 in 0 0 in 1 1 in 0 1 in 1 2 in 0 2 in 1 3 in 0
Resistance cells in time0 0 2655 0 616 0 297 0
Sensitive cells in time0 740 0 262 0 48 0 43
metacell_label2
condition 3 in 1 4 in 1 5 in 1
Resistance cells in time0 213 194 19
Sensitive cells in time0 0 0 0
In [261]:
max(nchar(unlist(chord_data$metacell_label2)))
6
In [252]:
dim(adjacencyData)
- 2
- 10
In [270]:
grid.col <-c( brewer.pal(8, "Set2"),brewer.pal(4, "Set3"))
grid.col
- '#66C2A5'
- '#FC8D62'
- '#8DA0CB'
- '#E78AC3'
- '#A6D854'
- '#FFD92F'
- '#E5C494'
- '#B3B3B3'
- '#8DD3C7'
- '#FFFFB3'
- '#BEBADA'
- '#FB8072'
In [271]:
#https://www.jianshu.com/p/73c246b87d82
#circos.clear()#for Error: Since parameter has length larger than 1, it should have same length as the number of sectors.gap.degree
chordDiagram(
adjacencyData, grid.col = grid.col,
annotationTrack = c("grid"),
preAllocateTracks = list(
track.height = max(strwidth(chord_data$metacell_label2))
)
)
circos.track(
track.index = 1, panel.fun = function(x, y) {
sector.name = get.cell.meta.data("sector.index")
if (nchar(sector.name) > max(nchar(unlist(chord_data$metacell_label2)))){
message(sector.name)
circos.text(
CELL_META$xcenter, CELL_META$ylim[1],
CELL_META$sector.index, facing = "inside",
niceFacing = TRUE, adj = c(0.5, 0)
)
}else{
circos.text(
CELL_META$xcenter, CELL_META$ylim[1],
CELL_META$sector.index, facing = "clockwise",
niceFacing = TRUE, adj = c(0, 0.5)
)
}
}, bg.border = NA
)
Resistance cells in time0 Sensitive cells in time0
In [273]:
pdf('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/res_orgin_state_transition_adata_meta.pdf')
chordDiagram(
adjacencyData, grid.col = grid.col,
annotationTrack = c("grid"),
preAllocateTracks = list(
track.height = max(strwidth(chord_data$metacell_label2))
)
)
circos.track(
track.index = 1, panel.fun = function(x, y) {
sector.name = get.cell.meta.data("sector.index")
if (nchar(sector.name) > max(nchar(unlist(chord_data$metacell_label2)))){
message(sector.name)
circos.text(
CELL_META$xcenter, CELL_META$ylim[1],
CELL_META$sector.index, facing = "inside",
niceFacing = TRUE, adj = c(0.5, 0)
)
}else{
circos.text(
CELL_META$xcenter, CELL_META$ylim[1],
CELL_META$sector.index, facing = "clockwise",
niceFacing = TRUE, adj = c(0, 0.5)
)
}
}, bg.border = NA
)
dev.off()
Resistance cells in time0 Sensitive cells in time0
png: 2
survival¶
In [1255]:
probeMap <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUSC/gencode.v22.annotation.gene.probeMap')
GDC_phenotype <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUSC/TCGA-LUSC.GDC_phenotype.tsv')
htseq_counts <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUSC/TCGA-LUSC.htseq_counts.tsv')
survival <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUSC/TCGA-LUSC.survival.tsv')
In [1256]:
ann_htseq_counts <- merge(htseq_counts,probeMap,by.x = 'Ensembl_ID',by.y = 'id',all = F)
In [1257]:
ann_htseq_counts <- ann_htseq_counts %>% as.data.frame()
In [1258]:
ann_htseq_counts <- ann_htseq_counts[,c('gene',colnames(htseq_counts)[2:ncol(htseq_counts)])]
In [1259]:
#ann_htseq_counts <- aggregate(.~gene,mean,data=ann_htseq_counts)
In [1260]:
drivers$V1
- 'CD24'
- 'GTF2B'
- 'MSH6'
- 'TFDP1'
- 'MYO1B'
- 'TUBA1B'
- 'SLC1A5'
- 'TGM2'
- 'BARD1'
- 'TUBB4B'
- 'RRBP1'
- 'ANXA2'
- 'PLK2'
- 'PSMC4'
- 'DGKE'
- 'CEBPD'
- 'HSPA2'
- 'RAB31'
- 'TFAP2C'
- 'CD9'
- 'TPM1'
- 'LMO7'
- 'KLF5'
- 'TRIB1'
- 'HIST1H1C'
- 'DAAM1'
In [1261]:
score_df <- ann_htseq_counts %>% dplyr::filter(gene %in% drivers$V1) %>% merge(y=drivers,by.x = 'gene',by.y = 'V1',all = F)
In [1262]:
score <- apply(score_df[,2:ncol(ann_htseq_counts)],MARGIN = 2,FUN = FUN5)
In [1263]:
score
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- TCGA-77-8009-01A
- -0.00670261133751277
- TCGA-52-7810-01A
- -0.000336835482975395
- TCGA-85-A50M-01A
- -0.000935789555992331
- TCGA-18-3415-01A
- -0.00168916394901437
- TCGA-63-5131-01A
- -0.00622277908746876
- TCGA-21-5782-01A
- -0.00283118192789117
- TCGA-94-A5I6-01A
- -0.010976837736865
- TCGA-NC-A5HN-01A
- -0.0106605160709217
- TCGA-94-8490-01A
- -0.00407809787911566
- TCGA-77-7335-11A
- 0.00533074502266732
- TCGA-92-7340-01A
- 0.0002937382561447
- TCGA-21-1077-01A
- -0.00379689474637601
- TCGA-21-1081-01A
- 0.00031541358307842
- TCGA-21-1075-01A
- -0.00918192653333793
- TCGA-39-5028-01A
- -0.00481868815060863
- TCGA-34-7107-01A
- -0.00614250916945924
- TCGA-85-A50Z-01A
- -0.0119042119646638
- TCGA-85-A4CN-01A
- -0.00386254195686879
- TCGA-33-AASB-01A
- -0.00291176196063763
- TCGA-60-2704-01A
- 0.00303748742752354
- TCGA-56-7580-01A
- 0.000355414039160036
- TCGA-66-2744-01A
- -0.016171618525471
- TCGA-34-8455-01A
- -0.0145763996604728
- TCGA-66-2791-01A
- -0.000328499415664355
- TCGA-34-5234-01A
- -0.00769131782973983
- TCGA-43-3394-01A
- -0.00781081905721949
- TCGA-77-A5FZ-01A
- -0.00395329455966808
- TCGA-63-7021-01A
- -0.00519627075802018
- TCGA-33-A5GW-01A
- 0.00033159771282441
- TCGA-85-6560-01A
- 0.00267008369982916
- TCGA-56-7222-01A
- 0.000538187951940856
- TCGA-63-7023-01A
- 0.00209261558246557
- TCGA-60-2697-01A
- -0.00348980990487599
- TCGA-22-5492-01A
- -0.000972962371185705
- TCGA-63-A5MR-01A
- -0.000990391513036085
- TCGA-33-6738-01A
- -0.00472600779096075
- TCGA-77-8008-01A
- -0.0043490724220707
- TCGA-63-A5MW-01A
- 0.000745919904134172
- TCGA-33-AASJ-01A
- -0.00524544894691242
- TCGA-33-4566-01A
- -0.000674480243187447
- TCGA-MF-A522-01A
- -0.00417287255613402
- TCGA-66-2790-01A
- -0.00172348698473799
- TCGA-85-8354-01A
- -0.0100672222176681
- TCGA-46-3767-01A
- -0.00858211582707947
- TCGA-85-8580-01A
- -0.00239913982512031
- TCGA-85-7710-11A
- -0.00608433970277905
- TCGA-33-4586-01A
- -0.0017934098303018
- TCGA-66-2795-01A
- -0.00283247145715053
- TCGA-39-5021-01A
- -0.00398657470202642
- TCGA-60-2710-01A
- -0.00668413786572593
- TCGA-66-2763-01A
- -0.00551778038045934
- TCGA-33-AASL-01A
- -0.00257461899428765
- TCGA-98-A539-01A
- -0.00336529627290378
- TCGA-39-5035-01A
- 0.000313558336718051
- TCGA-77-8139-01A
- 0.00381770203201496
- TCGA-98-A53C-01A
- 0.00196003552905635
- TCGA-43-8115-01A
- -0.00306678677457656
- TCGA-66-2782-01A
- -0.0113375957144277
- TCGA-98-A53J-01A
- -0.00720081287478576
- TCGA-96-A4JK-01A
- -0.00765823173738483
- TCGA-56-A4BW-01A
- 0.00159561435626046
- TCGA-94-A5I4-01A
- 0.00166097248290826
- TCGA-90-A4EE-01A
- -0.00877451720300763
- TCGA-43-7658-01A
- -0.00408734690578838
- TCGA-66-2754-01A
- -0.0020919460065147
- TCGA-85-A510-01A
- 0.000557399764792819
- TCGA-22-5478-01A
- 0.00936072318845609
- TCGA-66-2769-01A
- -0.00995424188764054
- TCGA-85-8664-01A
- 0.00431527752500009
- TCGA-56-8623-11A
- -0.00176404992724596
- TCGA-63-6202-01A
- -0.00530448071884858
- TCGA-66-2771-01A
- 0.000744735096184188
- TCGA-22-5482-11A
- -0.00484773949688748
- TCGA-21-1071-01A
- -0.0046118268481015
- TCGA-43-A56V-01A
- -0.00114000227203929
- TCGA-34-8454-01A
- 0.0031537790463692
- TCGA-43-5670-11A
- 0.00024667363493868
- TCGA-33-6737-11A
- -0.00272131753482303
- TCGA-18-4083-01A
- -0.00231608311160859
- TCGA-85-8353-01A
- -0.000684721759082678
- TCGA-22-1017-01A
- 0.00585710720867996
- TCGA-98-8022-01A
- -0.00317241541478636
- TCGA-60-2721-01A
- -0.00772665150519769
- TCGA-22-A5C4-01A
- -0.0133236086261001
- TCGA-O2-A5IB-01A
- -0.00910776144252761
- TCGA-46-3765-01A
- -0.00588300081467569
- TCGA-39-5029-01A
- -0.0016236476579744
- TCGA-22-4605-01A
- 0.000664149985580346
- TCGA-96-8170-01A
- -0.00307725164372667
- TCGA-34-5236-01A
- -0.0080781621806133
- TCGA-77-7463-01A
- -0.000762903756072911
- TCGA-37-4132-01A
- -0.00881194763412812
- TCGA-66-2778-01A
- -0.0107801025326196
- TCGA-37-5819-01A
- -0.00446843683813398
- TCGA-43-6773-01A
- 0.00565286035855402
- TCGA-39-5040-11A
- -0.00529658649060987
- TCGA-NC-A5HI-01A
- -0.00673249952228502
- TCGA-56-7582-11A
- -0.0113900432048767
- TCGA-77-A5G3-01A
- -0.00386249827250846
- TCGA-21-1070-01A
- -0.00884024257635302
- TCGA-60-2715-01A
- -0.00455173145977603
- TCGA-60-2712-01A
- -0.00164345738082029
- TCGA-60-2707-01A
- -0.0100152570757637
- TCGA-63-5128-01A
- -0.00125169916757649
- TCGA-63-A5MH-01A
- -0.00550024628204322
- TCGA-39-5034-01A
- -0.000388290400689806
- TCGA-90-6837-11A
- -0.00383371930726554
- TCGA-60-2723-01A
- -0.0187798672130036
- TCGA-85-A4QQ-01A
- 0.000290623263558416
- TCGA-18-3410-01A
- -0.0105388453792112
- TCGA-22-5485-01A
- 0.00217921554515213
- TCGA-85-6798-01A
- -0.00344760310706051
- TCGA-66-2788-01A
- -0.00885470633648343
- TCGA-77-8131-01A
- -0.00580554775322104
- TCGA-46-3769-01A
- -0.0054854786329607
- TCGA-85-8048-01A
- -0.0113964152082343
- TCGA-22-5491-11A
- -0.0133981809561774
- TCGA-43-A475-01A
- 0.0018854999365596
- TCGA-43-2576-01A
- 0.000126974916963481
- TCGA-39-5030-01A
- -0.00605780241274647
- TCGA-85-A4JC-01A
- -0.00443993627647879
- TCGA-56-8628-01A
- -0.00348046074398318
- TCGA-66-2783-01A
- 0.00248063857024004
- TCGA-56-8083-01A
- -0.00280583383459152
- TCGA-77-8143-01A
- -0.00338663862356413
- TCGA-94-A4VJ-01A
- -0.00328511475193109
- TCGA-66-2777-01A
- -0.00149599742744529
- TCGA-21-1080-01A
- 0.000635824876824649
- TCGA-98-A53D-01A
- -0.00602459628699935
- TCGA-56-A4BX-01A
- -0.00580821168049421
- TCGA-77-7337-01A
- -0.00901408429119115
- TCGA-77-8145-01A
- 0.000541723994044196
In [1264]:
survival <- survival[match(names(score),survival$sample),]
survival$score <- score
In [1265]:
survival <- survival %>% na.omit()
In [1266]:
median(score)
-0.00382616957182098
In [1267]:
survival$group<- ifelse((survival$score < median(score)), 1,2)
In [1268]:
survival
| sample | OS | _PATIENT | OS.time | score | group |
|---|---|---|---|---|---|
| <chr> | <int> | <chr> | <int> | <dbl> | <dbl> |
| TCGA-77-A5GA-01A | 0 | TCGA-77-A5GA | 1280 | -4.184020e-03 | 1 |
| TCGA-58-8387-01A | 1 | TCGA-58-8387 | 403 | -1.228141e-02 | 1 |
| TCGA-22-4599-01A | 1 | TCGA-22-4599 | 1161 | -8.511640e-03 | 1 |
| TCGA-77-7142-11A | 0 | TCGA-77-7142 | 2227 | 8.857295e-04 | 2 |
| TCGA-NC-A5HJ-01A | 1 | TCGA-NC-A5HJ | 418 | -3.996577e-03 | 1 |
| TCGA-77-A5G6-01A | 1 | TCGA-77-A5G6 | 678 | -5.706283e-03 | 1 |
| TCGA-O2-A52Q-01A | 1 | TCGA-O2-A52Q | 113 | 9.227783e-04 | 2 |
| TCGA-90-7769-01A | 0 | TCGA-90-7769 | 358 | -6.537740e-03 | 1 |
| TCGA-56-8504-01A | 0 | TCGA-56-8504 | 510 | -4.557203e-03 | 1 |
| TCGA-22-5472-11A | 1 | TCGA-22-5472 | 1975 | -5.643610e-04 | 2 |
| TCGA-77-A5GF-01A | 1 | TCGA-77-A5GF | 840 | -4.576073e-03 | 1 |
| TCGA-34-7107-11A | 1 | TCGA-34-7107 | 34 | -1.610330e-03 | 2 |
| TCGA-66-2800-01A | 0 | TCGA-66-2800 | 1492 | -9.082825e-03 | 1 |
| TCGA-85-7697-01A | 0 | TCGA-85-7697 | 1063 | -7.661543e-03 | 1 |
| TCGA-85-8049-01A | 0 | TCGA-85-8049 | 579 | -7.278028e-03 | 1 |
| TCGA-18-4086-01A | 1 | TCGA-18-4086 | 85 | -8.983801e-03 | 1 |
| TCGA-63-A5MJ-01A | 0 | TCGA-63-A5MJ | 1824 | -8.501698e-03 | 1 |
| TCGA-98-8020-01A | 1 | TCGA-98-8020 | 84 | -4.834184e-04 | 2 |
| TCGA-34-8454-11A | 0 | TCGA-34-8454 | 1180 | -1.785622e-03 | 2 |
| TCGA-90-6837-01A | 0 | TCGA-90-6837 | 758 | -1.166233e-02 | 1 |
| TCGA-66-2785-01A | 0 | TCGA-66-2785 | 60 | -8.017311e-03 | 1 |
| TCGA-68-8250-01A | 0 | TCGA-68-8250 | 244 | -1.724329e-03 | 2 |
| TCGA-56-A5DR-01A | 0 | TCGA-56-A5DR | 4 | -3.930295e-03 | 1 |
| TCGA-33-4589-01A | 1 | TCGA-33-4589 | 47 | -1.449521e-02 | 1 |
| TCGA-NK-A5D1-01A | 0 | TCGA-NK-A5D1 | 511 | -1.703163e-03 | 2 |
| TCGA-39-5022-01A | 1 | TCGA-39-5022 | 1679 | 9.089691e-06 | 2 |
| TCGA-L3-A4E7-01A | 0 | TCGA-L3-A4E7 | 392 | -3.770393e-03 | 2 |
| TCGA-39-5037-01A | 0 | TCGA-39-5037 | 1690 | -7.669179e-03 | 1 |
| TCGA-43-6143-11A | 0 | TCGA-43-6143 | 699 | -2.808283e-03 | 2 |
| TCGA-77-7338-11A | 1 | TCGA-77-7338 | 5 | 2.279184e-03 | 2 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| TCGA-66-2767-01A | 0 | TCGA-66-2767 | 61 | -0.0100152571 | 1 |
| TCGA-77-7142-01A | 0 | TCGA-77-7142 | 2227 | -0.0012516992 | 2 |
| TCGA-22-5477-01A | 1 | TCGA-22-5477 | 1346 | -0.0055002463 | 1 |
| TCGA-56-7222-11A | 1 | TCGA-56-7222 | 562 | -0.0003882904 | 2 |
| TCGA-85-7698-01A | 0 | TCGA-85-7698 | 952 | -0.0038337193 | 1 |
| TCGA-66-2766-01A | 0 | TCGA-66-2766 | 31 | -0.0187798672 | 1 |
| TCGA-56-7731-11A | 1 | TCGA-56-7731 | 3 | 0.0002906233 | 2 |
| TCGA-NC-A5HL-01A | 1 | TCGA-NC-A5HL | 88 | -0.0105388454 | 1 |
| TCGA-34-5239-01A | 0 | TCGA-34-5239 | 1834 | 0.0021792155 | 2 |
| TCGA-56-8308-01A | 0 | TCGA-56-8308 | 517 | -0.0034476031 | 2 |
| TCGA-L3-A524-01A | 1 | TCGA-L3-A524 | 490 | -0.0088547063 | 1 |
| TCGA-37-3789-01A | 0 | TCGA-37-3789 | 13 | -0.0058055478 | 1 |
| TCGA-77-8007-11A | 1 | TCGA-77-8007 | 198 | -0.0054854786 | 1 |
| TCGA-77-8128-01A | 1 | TCGA-77-8128 | 1150 | -0.0113964152 | 1 |
| TCGA-60-2713-01A | 0 | TCGA-60-2713 | 1731 | -0.0133981810 | 1 |
| TCGA-77-8146-01A | 0 | TCGA-77-8146 | 3189 | 0.0018854999 | 2 |
| TCGA-NC-A5HM-01A | 0 | TCGA-NC-A5HM | 1212 | 0.0001269749 | 2 |
| TCGA-92-7341-01A | 0 | TCGA-92-7341 | 106 | -0.0060578024 | 1 |
| TCGA-94-8035-01A | 0 | TCGA-94-8035 | 122 | -0.0044399363 | 1 |
| TCGA-60-2722-01A | 0 | TCGA-60-2722 | 908 | -0.0034804607 | 2 |
| TCGA-51-4079-11A | 1 | TCGA-51-4079 | 12 | 0.0024806386 | 2 |
| TCGA-77-7465-01A | 0 | TCGA-77-7465 | 990 | -0.0028058338 | 2 |
| TCGA-63-A5MG-01A | 0 | TCGA-63-A5MG | 2148 | -0.0033866386 | 2 |
| TCGA-56-7579-01A | 1 | TCGA-56-7579 | 951 | -0.0032851148 | 2 |
| TCGA-77-8148-01A | 0 | TCGA-77-8148 | 2023 | -0.0014959974 | 2 |
| TCGA-39-5039-01A | 1 | TCGA-39-5039 | 544 | 0.0006358249 | 2 |
| TCGA-43-8118-01A | 1 | TCGA-43-8118 | 89 | -0.0060245963 | 1 |
| TCGA-43-6647-01A | 0 | TCGA-43-6647 | 757 | -0.0058082117 | 1 |
| TCGA-18-3406-01A | 1 | TCGA-18-3406 | 371 | -0.0090140843 | 1 |
| TCGA-56-7730-11A | 1 | TCGA-56-7730 | 198 | 0.0005417240 | 2 |
In [1269]:
library(survival)
In [1270]:
fit.surv <-Surv(survival$OS.time,survival$OS)
In [1271]:
km<-survfit(fit.surv~1,data = survival)
km_2<- survfit(fit.surv~group,data=survival)
In [1272]:
library(survminer)
Loading required package: ggpubr
Attaching package: ‘survminer’
The following object is masked from ‘package:survival’:
myeloma
In [1273]:
ggsurvplot (km)
In [1274]:
ggsurvplot(km_2)
In [1275]:
p <- ggsurvplot(km_2, main = "Survival curve",
conf.int = TRUE,# 可信区间
palette = "npg",# 支持ggsci配色,自定义颜色,brewer palettes中的配色,等
ggtheme = theme_classic(), # 支持ggplot2及其扩展包的主题
legend.title = "Score",#改变图例名称
legend.labs = c("Low", "High"),
risk.table = TRUE, # 增加risk table
risk.table.title = "Risk set sizes",
ncensor.plot = TRUE, #增加删失事件表
surv.median.line = "hv",
pval=TRUE, #添加P值
log.rank.weights = "1", #pval.method
)
p
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?”
In [1276]:
#https://github.com/kassambara/survminer/issues/152
# add method to grid.draw
grid.draw.ggsurvplot <- function(x){
survminer:::print.ggsurvplot(x, newpage = FALSE)
}
# Remember to pass object `p`.
ggsave(
filename =paste0('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/survplot_TCGA_LUSC_byScore.pdf'),
plot = p,
device = 'pdf',
#path = 'data/output',
width = 8,
height = 9
)
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?”
In [1277]:
probeMap <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUNG/probeMap_hugo_gencode_good_hg19_V24lift37_probemap')
GDC_phenotype <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUNG/TCGA.LUNG.sampleMap_LUNG_clinicalMatrix')
htseq_counts <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUNG/TCGA.LUNG.sampleMap_HiSeqV2_PANCAN')
survival <- fread('/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master/cycleCancer_net/TCGA/LUNG/survival_LUNG_survival.txt')
In [1278]:
htseq_counts
| sample | TCGA-NJ-A4YP-01 | TCGA-18-3417-01 | TCGA-22-4613-01 | TCGA-90-7769-01 | TCGA-62-8397-01 | TCGA-77-A5G1-01 | TCGA-86-8278-01 | TCGA-77-A5G3-01 | TCGA-05-4430-01 | ⋯ | TCGA-63-A5MY-01 | TCGA-50-6593-01 | TCGA-33-AASL-01 | TCGA-85-A512-01 | TCGA-85-8354-01 | TCGA-O2-A5IB-01 | TCGA-67-3771-01 | TCGA-77-7335-01 | TCGA-55-8302-01 | TCGA-56-7731-11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| ARHGEF10L | -0.21369247 | -2.03299247 | -1.10919247 | -1.270392470 | 0.67710753 | 0.054707530 | 0.36190753 | -1.34419247 | -0.49769247 | ⋯ | -1.3352925 | 0.57120753 | -0.76229247 | -1.28699247 | -1.847792470 | 0.568507530 | -0.5214925 | -0.84989247 | 0.305607530 | -0.88899247 |
| HIF3A | -2.78002634 | -0.77512634 | -1.02342634 | -3.254826336 | 1.09297366 | 0.075173664 | -0.59692634 | 0.82617366 | -0.14642634 | ⋯ | 4.0218737 | 2.95837366 | -0.83192634 | -1.86342634 | -1.297326336 | 0.025973664 | 5.7299737 | -2.41412634 | -0.018326336 | 4.22947366 |
| RNF17 | -0.53103501 | 0.57376499 | -0.53103501 | -0.090835006 | -0.53103501 | -0.531035006 | -0.19843501 | -0.53103501 | -0.53103501 | ⋯ | 1.5540650 | -0.53103501 | 0.98446499 | -0.53103501 | -0.531035006 | 0.084864994 | 0.1927650 | 0.19286499 | -0.531035006 | -0.53103501 |
| RNF10 | 0.04072801 | 0.36522801 | -0.32677199 | 0.160728014 | 0.58352801 | -0.147471986 | -0.57507199 | -0.36467199 | -0.30007199 | ⋯ | -0.2929720 | -0.22177199 | -0.65157199 | 0.02032801 | -0.280071986 | -0.009371986 | -0.2792720 | 0.19722801 | 0.003728014 | 0.16222801 |
| RNF11 | -0.69937813 | 0.36452187 | 0.30812187 | 0.368321865 | 0.31492187 | 0.826221865 | -0.78377813 | -0.31297813 | 0.29642187 | ⋯ | 0.4791219 | -0.06717813 | -0.44667813 | -0.56097813 | -0.560278135 | -0.136278135 | 0.4865219 | 0.44392187 | -0.451578135 | 0.34452187 |
| RNF13 | -0.42950991 | 0.31389009 | 1.19219009 | -0.526209910 | -0.66210991 | 0.389290090 | -0.13280991 | 0.57109009 | 0.31579009 | ⋯ | 0.8531901 | 0.14669009 | 0.98119009 | 0.51729009 | 0.850090090 | -1.418109910 | 1.5721901 | 0.54789009 | -0.386709910 | 0.42709009 |
| GTF2IP1 | -0.38489449 | -0.24599449 | -0.81429449 | 0.506405509 | 0.02610551 | -0.713794491 | -0.99929449 | -0.24759449 | 0.31530551 | ⋯ | -0.4329945 | -0.14209449 | 0.53560551 | 0.51980551 | -0.166294491 | 0.173505509 | -0.7797945 | 0.08440551 | -0.084494491 | -0.91039449 |
| REM1 | 0.70235434 | 0.01625434 | -0.61824566 | -1.370945657 | -1.48874566 | 0.012254343 | -2.13474566 | -1.98774566 | 1.31905434 | ⋯ | -1.3584457 | 0.36575434 | 1.58005434 | -0.74814566 | -1.519245657 | -2.194045657 | -0.3241457 | 1.01505434 | -0.217745657 | 0.19415434 |
| MTVR2 | -0.42339941 | -0.42339941 | 1.04640059 | 0.016800593 | -0.42339941 | -0.423399407 | 0.17930059 | 0.46660059 | -0.42339941 | ⋯ | -0.4233994 | 0.29150059 | 1.09210059 | -0.42339941 | 0.602100593 | 0.622700593 | -0.4233994 | -0.42339941 | -0.423399407 | 0.46840059 |
| RTN4RL2 | 2.88832846 | -1.11657154 | -2.14307154 | -3.830171541 | 1.38192846 | -0.968671541 | 2.44202846 | -0.64517154 | 2.57392846 | ⋯ | -4.3611715 | 2.19452846 | -0.39437154 | -2.22857154 | -2.013971541 | 2.913828459 | 1.9295285 | 0.13072846 | -0.706871541 | -1.12847154 |
| C16orf13 | 0.49771071 | 0.53551071 | -0.58188929 | -0.112389286 | 0.22271071 | -1.224589286 | -0.30338929 | -0.41028929 | 0.46571071 | ⋯ | -0.6639893 | 0.48841071 | 0.30781071 | 0.32871071 | -0.134489286 | -1.419589286 | -1.3920893 | -0.04558929 | 0.641410714 | -0.33348929 |
| C16orf11 | -0.54308412 | -0.54308412 | -0.54308412 | 0.506715880 | -0.54308412 | -0.019884120 | -0.54308412 | -0.03048412 | -0.54308412 | ⋯ | -0.5430841 | -0.54308412 | -0.54308412 | 0.45201588 | -0.543084120 | -0.543084120 | -0.5430841 | -0.54308412 | -0.543084120 | -0.54308412 |
| FGFR1OP2 | -0.32215383 | 1.14464617 | 0.20134617 | 0.162946172 | -0.29635383 | 0.872246172 | -0.47965383 | 0.18504617 | -0.05265383 | ⋯ | 0.5179462 | -0.44905383 | -0.43445383 | 1.10694617 | 1.720346172 | -0.336853828 | -0.2824538 | 0.09074617 | -0.146953828 | 0.28224617 |
| TSKS | -0.09128716 | -1.56558716 | 0.61701284 | -1.565587156 | 0.40511284 | -1.042387156 | 0.27951284 | -0.67558716 | -0.18988716 | ⋯ | -1.5655872 | 0.50731284 | -0.05008716 | 0.23471284 | 0.643912844 | -1.565587156 | 2.5994128 | 0.28491284 | 1.015512844 | 0.26891284 |
| ATRX | -0.15600253 | -1.50410253 | -0.99640253 | -1.523502528 | -0.07540253 | -0.048702528 | 0.81019747 | 0.67369747 | -0.42180253 | ⋯ | -0.3381025 | -0.30870253 | -0.71980253 | -0.21970253 | -0.300802528 | 0.962397472 | -0.4801025 | -0.09250253 | -0.619302528 | -0.16670253 |
| PMM2 | 1.40584152 | 1.11474152 | 0.06594152 | 0.456741522 | -0.34375848 | 1.281841522 | 0.79294152 | 0.32324152 | 0.21664152 | ⋯ | 0.3174415 | 0.18094152 | 0.01734152 | 0.31094152 | 0.478041522 | -0.920058478 | 0.4083415 | -0.02915848 | 0.338041522 | 0.81724152 |
| LOC100272146 | 0.88265240 | -0.88374760 | -1.00764760 | -0.331247600 | -1.34104760 | 0.441552400 | 0.38065240 | 0.08965240 | -1.22204760 | ⋯ | -0.0601476 | 0.09295240 | 0.58345240 | 0.92565240 | 0.545252400 | 0.360752400 | 1.2801524 | -0.99744760 | 1.080752400 | -1.46474760 |
| ASS1 | 2.31555096 | 0.55765096 | -0.36884904 | 0.734950956 | 1.69325096 | -0.046249044 | 1.67695096 | 0.53495096 | 1.93435096 | ⋯ | -3.0288490 | -0.20114904 | 2.22865096 | 1.25725096 | -0.377749044 | -1.331849044 | 0.9728510 | -0.84344904 | -0.273349044 | 0.88875096 |
| NCBP1 | 0.03239523 | -0.34780477 | 0.51649523 | 0.639695232 | -1.29290477 | -0.392004768 | 0.31489523 | 0.98959523 | 0.17259523 | ⋯ | -0.7460048 | -0.64950477 | 0.54659523 | 0.77769523 | 0.469395232 | 0.182095232 | -0.6199048 | 0.65249523 | -0.858204768 | -0.70490477 |
| ZNF709 | -1.09256045 | -3.34196045 | 0.65003955 | -3.578760453 | 0.12043955 | -1.006260453 | -0.15886045 | -1.29916045 | 0.22233955 | ⋯ | -0.1757605 | -0.51306045 | -0.97916045 | -1.33426045 | -0.454660453 | 0.842339547 | 0.3474395 | 0.20233955 | -0.754260453 | -1.68346045 |
| ZNF708 | -0.30639890 | -2.98429890 | -0.01029890 | -1.132098900 | -1.14749890 | -0.957098900 | 0.71190110 | -0.32529890 | -0.01249890 | ⋯ | -1.8066989 | -0.74349890 | -0.40919890 | 0.19460110 | -1.996498900 | 0.631301100 | -0.1454989 | 0.00490110 | -0.247498900 | -0.96269890 |
| RBM14 | -0.24472118 | -0.46482118 | 0.05797882 | -0.008821178 | -0.26042118 | -0.337721178 | -0.01622118 | 0.25197882 | -0.11462118 | ⋯ | -0.3795212 | -0.26252118 | 0.03917882 | 0.32487882 | 0.800178822 | 0.563678822 | -0.2728212 | -0.09792118 | 0.068078822 | -0.02812118 |
| NCBP2 | 0.09082893 | 0.11492893 | 1.44862893 | 0.776328935 | -0.80507107 | 0.952328935 | 0.12232893 | 1.10792893 | -0.16297107 | ⋯ | 1.5282289 | 0.02922893 | 1.59492893 | 0.64232893 | 0.501328935 | -0.093871065 | 1.0536289 | 1.39772893 | -0.195671065 | -0.05667107 |
| DISC1 | 0.73228609 | -1.68861391 | 0.45078609 | -1.374913906 | -0.16911391 | -1.004313906 | -0.12131391 | -0.57021391 | -0.36191391 | ⋯ | -0.8773139 | -0.24531391 | -1.49991391 | 1.30418609 | -1.538513906 | -0.623313906 | 0.5632861 | -0.03331391 | -0.659213906 | -0.69791391 |
| CAMK1 | -0.75625345 | -0.42905345 | -1.09875345 | -2.213453445 | 0.28014655 | -0.880753445 | 0.12134655 | -1.06635345 | 0.91894655 | ⋯ | -1.0010534 | 0.63434655 | -0.90195345 | -0.33355345 | 0.086146555 | -1.897453445 | -0.7065534 | 0.13984655 | 0.467646555 | 0.62254655 |
| RPL37 | 0.75441135 | 1.63201135 | 1.00481135 | 1.682411354 | 0.50691135 | 0.417711354 | -0.22568865 | 0.50181135 | 1.00511135 | ⋯ | 2.0032114 | 1.18501135 | 0.89991135 | 0.14291135 | 0.501111354 | -0.732088646 | -0.8980886 | 0.47681135 | 0.016511354 | -0.35328865 |
| SPR | 0.85050614 | 1.74890614 | 0.87510614 | 0.519106141 | 0.71690614 | 0.005206141 | 0.32550614 | -0.05889386 | 0.28350614 | ⋯ | 0.3018061 | 0.42510614 | 1.52840614 | 1.34400614 | 0.556906141 | -2.203593859 | 0.1602061 | 0.60540614 | 0.575906141 | -0.49089386 |
| ZNF700 | 0.20937597 | -1.81582403 | 0.41927597 | -0.373024034 | -0.68872403 | -0.104324034 | -0.07102403 | 0.74767597 | -0.36182403 | ⋯ | 0.7786760 | -0.74442403 | 1.51807597 | 0.55597597 | 1.507575966 | 0.768675966 | 0.8267760 | -1.01532403 | -0.149624034 | -0.84362403 |
| ZNF707 | -0.11726627 | -1.70956627 | 0.30083373 | 0.139233734 | -0.02576627 | 0.269833734 | -0.03936627 | 0.32633373 | -0.90756627 | ⋯ | 0.9233337 | 0.36203373 | 1.36013373 | 0.38423373 | 0.004133734 | 0.686733734 | 0.2414337 | -0.94576627 | 0.936633734 | -0.54406627 |
| CAMK4 | -0.33729762 | -0.23089762 | 0.15240238 | 0.148602380 | -0.51469762 | 1.315802380 | 0.11300238 | 0.30270238 | -0.39799762 | ⋯ | -2.4900976 | -0.42919762 | 0.46130238 | 0.07560238 | 1.386702380 | 1.723102380 | -0.8621976 | 0.98030238 | -1.421497620 | 0.03300238 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| C8orf83 | 0.03290948 | 0.32060948 | -0.44609052 | -2.11409052 | -0.09959052 | -0.143490519 | 0.2177095 | -1.48989052 | 0.57100948 | ⋯ | -2.24969052 | 0.25460948 | -0.14239052 | -0.40879052 | -2.75329052 | -0.57079052 | 0.48360948 | -0.06379052 | 1.255009481 | -0.45389052 |
| C1orf192 | 0.10989140 | 1.28659140 | -0.66340860 | -0.76880860 | -0.70410860 | -1.201008599 | 1.0494914 | -1.48060860 | 3.69969140 | ⋯ | -0.96190860 | 2.38939140 | -3.50440860 | -0.81860860 | -1.09750860 | 0.46509140 | 0.12489140 | -0.72870860 | -1.212108599 | 2.67099140 |
| PLAA | 0.04717590 | 0.46387590 | -0.40722410 | 0.60587590 | 0.19177590 | 0.112775895 | -0.0782241 | 0.41727590 | -0.05912410 | ⋯ | 1.56607590 | -0.11452410 | 0.10247590 | -0.53052410 | -4.50632410 | 1.17717590 | 0.41177590 | 0.01827590 | -0.537924105 | 0.39647590 |
| C8orf80 | 0.66281659 | 0.08551659 | 2.44371659 | -1.45958341 | 0.55581659 | 0.310516590 | 2.1343166 | -0.65238341 | 0.13601659 | ⋯ | -1.03118341 | 0.22771659 | -0.54828341 | -0.68908341 | -1.22758341 | -1.39298341 | 2.87801659 | 1.71711659 | 0.678916590 | -1.12108341 |
| C8orf86 | -0.45138713 | -0.45138713 | -0.45138713 | -0.45138713 | -0.45138713 | -0.451387132 | -0.4513871 | 0.43861287 | -0.45138713 | ⋯ | -0.45138713 | -0.45138713 | -0.45138713 | -0.45138713 | -0.45138713 | -0.45138713 | -0.45138713 | -0.45138713 | -0.451387132 | -0.45138713 |
| C8orf85 | -1.77766307 | -1.52886307 | -0.40506307 | -0.66826307 | 2.56753693 | 0.735536925 | -1.3063631 | -0.54136307 | 1.00973693 | ⋯ | -2.30586307 | 2.02763693 | 1.17093693 | -2.25686307 | -1.04246307 | 3.68983693 | 1.06223693 | -1.40146307 | 0.468636925 | 0.18613693 |
| C8orf84 | -1.56227245 | -4.56197245 | 1.23302755 | -2.97487245 | -1.44027245 | -2.010172454 | -0.9391725 | 0.37072755 | 0.10062755 | ⋯ | -2.30087245 | -0.36257245 | 2.93382755 | 1.18082755 | -1.53587245 | -1.16357245 | -0.98357245 | 0.34992755 | -1.822772454 | 2.02962755 |
| NFIC | -0.14410189 | -0.64260189 | -0.24730189 | -0.01530189 | 0.23749811 | -0.577301888 | 1.0277981 | 0.24389811 | 0.61819811 | ⋯ | -2.00590189 | 0.61279811 | -1.50940189 | -0.52790189 | 0.07359811 | 1.48449811 | -0.45400189 | -0.34500189 | 0.001798112 | 1.02519811 |
| NFIB | 0.31388817 | -1.20851183 | -0.84801183 | -1.53141183 | -1.70541183 | -0.443911830 | 1.3089882 | -0.03711183 | 0.14648817 | ⋯ | -1.16131183 | 0.29248817 | -1.98031183 | 0.02148817 | 0.36468817 | 2.58358817 | 0.29758817 | 0.46798817 | -0.800411830 | 0.47078817 |
| H1FOO | -0.06373190 | -0.06373190 | -0.06373190 | -0.06373190 | -0.06373190 | -0.063731900 | -0.0637319 | -0.06373190 | -0.06373190 | ⋯ | -0.06373190 | -0.06373190 | -0.06373190 | -0.06373190 | -0.06373190 | -0.06373190 | -0.06373190 | -0.06373190 | -0.063731900 | -0.06373190 |
| NFIX | -1.10609312 | -1.12039312 | -0.39449312 | -1.50569312 | 0.73430688 | -1.586693117 | -0.2269931 | -0.07199312 | -0.52069312 | ⋯ | -1.64279312 | -0.68509312 | -1.20429312 | -0.70819312 | 0.71170688 | 0.63590688 | -1.08279312 | -0.19549312 | -1.759293117 | 0.13030688 |
| PLEKHG6 | -2.29898743 | 4.02121257 | 1.99571257 | 3.79631257 | 2.02511257 | 3.689112571 | 0.8403126 | 2.47781257 | 1.10951257 | ⋯ | 2.32401257 | 1.80701257 | 3.38491257 | 4.06351257 | 2.29741257 | 0.73881257 | 1.63491257 | 2.84901257 | 2.882812571 | 0.13031257 |
| PLEKHG7 | 2.03181071 | -1.42478929 | -0.65978929 | -1.75268929 | 2.45481071 | 0.210810714 | 3.1658107 | -0.69778929 | 2.78931071 | ⋯ | -0.15948929 | 3.48171071 | -1.36658929 | -1.21358929 | -1.50408929 | 4.29511071 | 4.97391071 | -0.23408929 | 1.697010714 | 1.50601071 |
| PLEKHG4 | -1.46484297 | 0.76185703 | 2.12695703 | 1.42405703 | 1.46435703 | -0.468342973 | 1.4031570 | 2.59915703 | 0.11375703 | ⋯ | 1.11765703 | 0.41355703 | 1.38405703 | 2.41445703 | 3.17285703 | 3.01505703 | 2.00355703 | -0.23194297 | 1.136257027 | -1.16154297 |
| PLEKHG5 | 0.29471306 | -1.39268694 | 0.18811306 | 1.20341306 | 0.56571306 | 1.215213055 | 0.6953131 | 1.19931306 | -0.43438694 | ⋯ | 0.99411306 | 0.30401306 | 1.31551306 | 1.77041306 | 1.22431306 | 2.67201306 | 1.70751306 | -0.26218694 | 1.027013055 | -0.82078694 |
| SLC7A14 | -1.99013046 | -1.99013046 | -1.99013046 | -1.54993046 | -1.99013046 | -1.466930464 | -1.9901305 | -1.47753046 | -1.99013046 | ⋯ | -1.99013046 | -1.99013046 | -1.99013046 | -0.99503046 | -1.99013046 | -1.64963046 | 2.32406954 | -0.13963046 | -1.990130464 | -1.99013046 |
| SELE | 1.82569117 | 0.48469117 | 0.92539117 | 0.75329117 | -2.64330883 | 2.244191174 | -0.8919088 | -1.95320883 | -0.65030883 | ⋯ | -2.08000883 | -0.33430883 | -0.52800883 | 1.02799117 | -1.26890883 | -3.42470883 | -1.55690883 | 0.27779117 | -2.629808826 | 5.41289117 |
| SLC7A10 | 2.27721425 | -2.09078575 | -2.09078575 | -1.29888575 | 2.44821425 | -0.868485751 | 0.7149142 | 0.31531425 | 6.26281425 | ⋯ | 0.54961425 | 2.03461425 | 0.18661425 | -2.09078575 | -0.73828575 | 0.38101425 | 2.55911425 | -2.09078575 | -1.454985751 | 0.43871425 |
| PLA2G2C | -0.08668212 | -0.08668212 | -0.08668212 | -0.08668212 | -0.08668212 | -0.086682123 | 0.5160179 | -0.08668212 | 1.09971788 | ⋯ | -0.08668212 | -0.08668212 | -0.08668212 | -0.08668212 | -0.08668212 | -0.08668212 | -0.08668212 | -0.08668212 | 0.549117877 | -0.08668212 |
| METTL11A | 0.56927707 | 0.34377707 | 0.59407707 | 0.55277707 | 0.19457707 | -0.334222926 | 0.2088771 | 0.51897707 | 0.55757707 | ⋯ | -0.40062293 | -0.26792293 | 0.37937707 | 0.74207707 | 0.67257707 | -1.71202293 | 0.15797707 | -0.52962293 | 0.481377074 | 0.11737707 |
| TULP2 | 0.06852169 | -0.74887831 | -0.74887831 | 0.02802169 | -0.74887831 | 0.157421693 | -0.4162783 | 0.14112169 | 0.43752169 | ⋯ | -0.74887831 | 0.79922169 | -0.74887831 | -0.74887831 | 0.27662169 | -0.40837831 | 3.24962169 | -0.74887831 | -0.748878307 | -0.74887831 |
| NPY5R | -1.58711704 | -1.58711704 | -1.58711704 | -1.58711704 | -1.58711704 | -1.587117043 | -1.5871170 | -1.58711704 | 0.10568296 | ⋯ | -1.58711704 | -0.87221704 | -0.89141704 | -1.58711704 | -1.58711704 | -1.24661704 | -0.86331704 | -1.58711704 | -1.587117043 | -0.39601704 |
| GNGT2 | 0.93176654 | 0.30496654 | 0.77966654 | -2.52823346 | 0.91336654 | 0.382066539 | 0.5875665 | -1.15863346 | 1.30426654 | ⋯ | -2.55633346 | 1.10506654 | -0.79953346 | -0.23603346 | -0.78413346 | -1.94053346 | 0.41606654 | 2.16036654 | 1.078866539 | 2.03046654 |
| GNGT1 | -0.46398982 | 2.60701018 | 1.15821018 | 2.07981018 | 0.01721018 | 1.361410183 | -1.2813898 | 0.99351018 | -0.56948982 | ⋯ | 2.61571018 | -1.28138982 | 2.70701018 | 1.41071018 | 1.46201018 | -1.28138982 | -1.28138982 | 1.19391018 | -1.281389817 | -1.28138982 |
| TULP3 | -0.00267684 | 1.30262316 | 0.12402316 | -0.43147684 | -0.33867684 | 0.588023160 | 0.4320232 | 0.58352316 | 0.02512316 | ⋯ | 0.22692316 | -0.31237684 | 0.11782316 | 1.65372316 | 0.76502316 | 1.28172316 | -0.51127684 | 0.19702316 | 0.280623160 | -0.79007684 |
| PTRF | 0.74571421 | 1.02501421 | -0.39668579 | 0.09471421 | -0.00518579 | 1.569614210 | 0.1083142 | 1.26991421 | 0.88161421 | ⋯ | -0.22258579 | 0.33231421 | 0.77471421 | 0.43441421 | 1.24691421 | -2.42558579 | -0.86608579 | 1.55011421 | 0.634014210 | 2.46851421 |
| BCL6B | 1.46177337 | -0.38102663 | -0.78132663 | -2.26782663 | -0.73522663 | -0.409226625 | -0.2908266 | -0.54212663 | 1.71117337 | ⋯ | -1.71472663 | 0.70387337 | -2.30672663 | -0.49142663 | -0.07002663 | -0.73442663 | 0.53747337 | 0.29477337 | -0.909526625 | 2.66797337 |
| GSTK1 | -0.42619462 | 0.83930538 | -0.73249462 | -1.13709462 | 0.20410538 | -0.003394623 | -1.3093946 | -1.32499462 | -0.50409462 | ⋯ | 0.25160538 | 0.30260538 | 0.40620538 | 0.31830538 | -0.62069462 | -1.92679462 | -1.50859462 | 0.36900538 | -0.114894623 | -0.26469462 |
| SELP | -0.64503328 | -1.08503328 | 1.73386672 | -1.21023328 | 1.33656672 | 1.761466719 | 0.1984667 | -1.78443328 | 2.47906672 | ⋯ | -0.01853328 | 1.73806672 | -2.34673328 | -1.01143328 | -1.39033328 | -2.12163328 | 0.35666672 | 1.90926672 | 0.848866719 | 3.93086672 |
| SELS | 0.40018760 | 0.53318760 | 0.62448760 | 0.15368760 | -0.59821240 | 0.738687600 | 0.4574876 | 0.88168760 | 0.31168760 | ⋯ | 0.51108760 | -0.37241240 | 0.87938760 | 0.39228760 | 0.91888760 | -0.63741240 | 0.78618760 | 0.11768760 | 0.195587600 | 0.84258760 |
In [1279]:
drivers$V1
- 'CD24'
- 'GTF2B'
- 'MSH6'
- 'TFDP1'
- 'MYO1B'
- 'TUBA1B'
- 'SLC1A5'
- 'TGM2'
- 'BARD1'
- 'TUBB4B'
- 'RRBP1'
- 'ANXA2'
- 'PLK2'
- 'PSMC4'
- 'DGKE'
- 'CEBPD'
- 'HSPA2'
- 'RAB31'
- 'TFAP2C'
- 'CD9'
- 'TPM1'
- 'LMO7'
- 'KLF5'
- 'TRIB1'
- 'HIST1H1C'
- 'DAAM1'
In [1280]:
score_df <- htseq_counts %>% dplyr::filter(gene %in% drivers$V1) %>% merge(y=drivers,by.x = 'sample',by.y = 'V1',all = F)
score <- apply(score_df[,2:ncol(htseq_counts)],MARGIN = 2,FUN = FUN1)
In [1281]:
score
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- 0.0138060572482492
- TCGA-34-A5IX-01
- 0.0162963743884066
- TCGA-56-8623-01
- -0.000635657590659697
- TCGA-63-A5MI-01
- 0.0155316218679957
- TCGA-55-8203-01
- 0.0184083627597658
- TCGA-66-2791-01
- 0.000396297662592241
- TCGA-21-1071-01
- 0.0177739229227877
- TCGA-60-2711-01
- 0.0166433786793905
- TCGA-49-6743-11
- -0.0130493768855369
- TCGA-37-4133-01
- 0.0118094501089099
- TCGA-34-8454-01
- 0.00561590065814699
- TCGA-55-6985-01
- 0.0147226845210072
- TCGA-77-8146-01
- 0.00689151481169684
- TCGA-L9-A5IP-01
- 0.0100857969593694
- TCGA-56-7582-01
- 0.013116887761903
- TCGA-75-5126-01
- 0.00865807201466872
- TCGA-18-3406-01
- 0.0261926966585413
- TCGA-56-8309-01
- 0.00848948434223168
- TCGA-73-4662-01
- 0.017708579340981
- TCGA-44-2655-01
- 0.000426282476396538
- TCGA-21-1080-01
- 0.00309150532565452
- TCGA-93-7347-01
- 0.0221064562030549
- TCGA-MP-A4TF-01
- 0.000182611979200796
- TCGA-78-7535-01
- 0.0213084704843989
- TCGA-60-2721-01
- 0.0123227609419139
- TCGA-60-2716-01
- 0.0147134505496912
- TCGA-62-A471-01
- 0.0159358077667271
- TCGA-49-4490-01
- 0.0118603519745776
- TCGA-66-2737-01
- 0.0110350230379942
- TCGA-63-7021-01
- 0.0161636099488254
- TCGA-86-8671-01
- 0.0226070265039971
- TCGA-73-4666-01
- 0.0258640619397138
- TCGA-85-8352-01
- 0.0035558164835295
- TCGA-85-7698-01
- 0.00506913723910223
- TCGA-38-4628-01
- 0.0105125920802997
- TCGA-77-8139-01
- 0.0135044278777492
- TCGA-34-7107-11
- 0.0211181229869924
- TCGA-64-5775-01
- 0.00456957378289423
- TCGA-86-7953-01
- 0.0160502204907882
- TCGA-38-6178-01
- 0.0120332634148462
- TCGA-44-7672-01
- 0.0122691819256837
- TCGA-98-8020-01
- 0.00862190988930192
- TCGA-85-8052-01
- 0.00648260142222829
- TCGA-55-6979-01
- 0.00519721766878458
- TCGA-55-7573-01
- -0.00422926604733469
- TCGA-55-8097-01
- 0.0181102090950499
- TCGA-43-3920-01
- 0.0071846147289651
- TCGA-77-7142-01
- -0.000635380376002356
- TCGA-97-7546-01
- 0.014645434794326
- TCGA-NC-A5HK-01
- 0.000661024056522213
- TCGA-05-5425-01
- 0.0184444447318634
- TCGA-55-6970-01
- 0.0147516656561938
- TCGA-43-7658-11
- 0.0211683245840567
- TCGA-60-2719-01
- 0.0178993146048817
- TCGA-50-5946-02
- 0.00777548825191501
- TCGA-58-8387-01
- 0.0115894698820752
- TCGA-55-1596-01
- 0.0218651109808439
- TCGA-43-7658-01
- 0.0107111842306313
- TCGA-78-7148-01
- 0.00983357311964689
- TCGA-96-A4JK-01
- 0.00629485857615775
- TCGA-50-5066-02
- 0.0108438577406798
- TCGA-50-5066-01
- 0.00605670811236899
- TCGA-43-6143-01
- 0.0075376116855206
- TCGA-56-8305-01
- 0.0165917244703022
- TCGA-55-8092-01
- 0.0180227324534112
- TCGA-50-8459-01
- 0.0196315039460845
- TCGA-90-6837-11
- 0.024714214274047
- TCGA-75-6207-01
- 0.00409518179896989
- TCGA-44-2665-11
- 0.0221599499068418
- TCGA-66-2769-01
- 0.0171233531584745
- TCGA-97-7938-01
- 0.00232362974349687
- TCGA-22-1000-01
- 0.0123949290130394
- TCGA-18-3412-01
- 0.0188560902448004
- TCGA-66-2759-01
- 0.0185974097104029
- TCGA-49-AARE-01
- 0.00602470048583457
- TCGA-NJ-A7XG-01
- 0.0119656199366797
- TCGA-66-2794-01
- 0.0149087396324592
- TCGA-21-1083-01
- -0.00192173740460946
- TCGA-60-2695-01
- 0.00615355005476825
- TCGA-50-5055-01
- 0.0135507877736679
- TCGA-85-6560-01
- 0.0192509798623795
- TCGA-98-8022-01
- 0.0073124514277153
- TCGA-91-8496-01
- 0.00836076355919284
- TCGA-43-5668-01
- 0.0059065595378326
- TCGA-21-5786-01
- 0.0145076564787722
- TCGA-05-4418-01
- 0.0198227123066194
- TCGA-77-8007-11
- -0.00352141114333758
- TCGA-34-8456-01
- 0.0222366525480339
- TCGA-69-7764-01
- 0.01457508445816
- TCGA-55-8620-01
- 0.004460156587941
- TCGA-95-7043-01
- 0.0066230619634217
- TCGA-66-2755-01
- 0.00968610337812382
- TCGA-18-4083-01
- 0.0174891777930031
- TCGA-86-7701-01
- 0.0101575235204199
- TCGA-56-7222-01
- 0.0134943831963811
- TCGA-52-7812-01
- 0.0124592501876909
- TCGA-94-8490-01
- 0.0199277866851551
- TCGA-73-4676-01
- 0.0157407043085665
- TCGA-44-6777-01
- 0.0160357368490573
- TCGA-51-4081-01
- 0.013989310155162
In [1282]:
survival
| sample | _PATIENT | OS | OS.time | DSS | DSS.time | DFI | DFI.time | PFI | PFI.time | Redaction |
|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <lgl> |
| TCGA-05-4244-01 | TCGA-05-4244 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | NA |
| TCGA-05-4249-01 | TCGA-05-4249 | 0 | 1523 | 0 | 1523 | NA | NA | 0 | 1523 | NA |
| TCGA-05-4250-01 | TCGA-05-4250 | 1 | 121 | NA | 121 | NA | NA | 0 | 121 | NA |
| TCGA-05-4382-01 | TCGA-05-4382 | 0 | 607 | 0 | 607 | 1 | 334 | 1 | 334 | NA |
| TCGA-05-4384-01 | TCGA-05-4384 | 0 | 426 | 0 | 426 | NA | NA | 1 | 183 | NA |
| TCGA-05-4389-01 | TCGA-05-4389 | 0 | 1369 | 0 | 1369 | NA | NA | 0 | 1369 | NA |
| TCGA-05-4390-01 | TCGA-05-4390 | 0 | 1126 | 0 | 1126 | NA | NA | 1 | 395 | NA |
| TCGA-05-4395-01 | TCGA-05-4395 | 1 | 0 | 0 | 0 | NA | NA | 0 | 0 | NA |
| TCGA-05-4396-01 | TCGA-05-4396 | 1 | 303 | NA | 303 | NA | NA | 0 | 303 | NA |
| TCGA-05-4397-01 | TCGA-05-4397 | 1 | 731 | NA | 731 | NA | NA | 0 | 731 | NA |
| TCGA-05-4398-01 | TCGA-05-4398 | 0 | 1431 | 0 | 1431 | 0 | 1431 | 0 | 1431 | NA |
| TCGA-05-4402-01 | TCGA-05-4402 | 1 | 244 | 0 | 244 | NA | NA | 0 | 244 | NA |
| TCGA-05-4403-01 | TCGA-05-4403 | 0 | 578 | 0 | 578 | NA | NA | 0 | 578 | NA |
| TCGA-05-4405-01 | TCGA-05-4405 | 0 | 610 | 0 | 610 | NA | NA | 0 | 610 | NA |
| TCGA-05-4410-01 | TCGA-05-4410 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | NA |
| TCGA-05-4415-01 | TCGA-05-4415 | 1 | 91 | 1 | 91 | NA | NA | 1 | 60 | NA |
| TCGA-05-4417-01 | TCGA-05-4417 | 0 | 455 | 0 | 455 | NA | NA | 0 | 455 | NA |
| TCGA-05-4418-01 | TCGA-05-4418 | 1 | 274 | NA | 274 | NA | NA | 0 | 274 | NA |
| TCGA-05-4420-01 | TCGA-05-4420 | 0 | 912 | 0 | 912 | 0 | 912 | 0 | 912 | NA |
| TCGA-05-4422-01 | TCGA-05-4422 | 0 | 365 | 0 | 365 | NA | NA | 0 | 365 | NA |
| TCGA-05-4424-01 | TCGA-05-4424 | 0 | 913 | 0 | 913 | NA | NA | 1 | 153 | NA |
| TCGA-05-4425-01 | TCGA-05-4425 | 0 | 669 | 0 | 669 | NA | NA | 0 | 669 | NA |
| TCGA-05-4426-01 | TCGA-05-4426 | 0 | 791 | 0 | 791 | 1 | 457 | 1 | 457 | NA |
| TCGA-05-4427-01 | TCGA-05-4427 | 0 | 791 | 0 | 791 | 0 | 791 | 0 | 791 | NA |
| TCGA-05-4430-01 | TCGA-05-4430 | 0 | 761 | 0 | 761 | NA | NA | 0 | 761 | NA |
| TCGA-05-4432-01 | TCGA-05-4432 | 0 | 761 | 0 | 761 | 0 | 761 | 0 | 761 | NA |
| TCGA-05-4433-01 | TCGA-05-4433 | 0 | 730 | 0 | 730 | NA | NA | 0 | 730 | NA |
| TCGA-05-4434-01 | TCGA-05-4434 | 1 | 457 | NA | 457 | NA | NA | 0 | 457 | NA |
| TCGA-05-5420-01 | TCGA-05-5420 | 0 | 457 | 0 | 457 | NA | NA | 1 | 245 | NA |
| TCGA-05-5420-11 | TCGA-05-5420 | 0 | 457 | 0 | 457 | NA | NA | 1 | 245 | NA |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| TCGA-LA-A7SW-01 | TCGA-LA-A7SW | 1 | 408 | 1 | 408 | NA | NA | 1 | 235 | NA |
| TCGA-MF-A522-01 | TCGA-MF-A522 | 1 | 360 | 1 | 360 | NA | NA | 1 | 173 | NA |
| TCGA-NC-A5HD-01 | TCGA-NC-A5HD | 1 | 2 | 0 | 2 | NA | NA | 0 | 2 | NA |
| TCGA-NC-A5HE-01 | TCGA-NC-A5HE | 0 | 2336 | 0 | 2336 | 0 | 2336 | 0 | 2336 | NA |
| TCGA-NC-A5HF-01 | TCGA-NC-A5HF | 1 | 138 | 1 | 138 | NA | NA | 1 | 132 | NA |
| TCGA-NC-A5HG-01 | TCGA-NC-A5HG | 0 | 1963 | 0 | 1963 | 0 | 1963 | 0 | 1963 | NA |
| TCGA-NC-A5HH-01 | TCGA-NC-A5HH | 0 | 37 | 0 | 37 | 0 | 37 | 0 | 37 | NA |
| TCGA-NC-A5HI-01 | TCGA-NC-A5HI | 0 | 1743 | 0 | 1743 | 0 | 1743 | 1 | 70 | NA |
| TCGA-NC-A5HJ-01 | TCGA-NC-A5HJ | 1 | 418 | 1 | 418 | 0 | 418 | 1 | 328 | NA |
| TCGA-NC-A5HK-01 | TCGA-NC-A5HK | 0 | 128 | 0 | 128 | 0 | 128 | 0 | 128 | NA |
| TCGA-NC-A5HL-01 | TCGA-NC-A5HL | 1 | 88 | 0 | 88 | NA | NA | 0 | 88 | NA |
| TCGA-NC-A5HM-01 | TCGA-NC-A5HM | 0 | 1212 | 0 | 1212 | 0 | 1212 | 0 | 1212 | NA |
| TCGA-NC-A5HN-01 | TCGA-NC-A5HN | 0 | 1499 | 0 | 1499 | 0 | 1499 | 0 | 1499 | NA |
| TCGA-NC-A5HO-01 | TCGA-NC-A5HO | 0 | 1336 | 0 | 1336 | 0 | 1336 | 0 | 1336 | NA |
| TCGA-NC-A5HP-01 | TCGA-NC-A5HP | 1 | 770 | 1 | 770 | NA | NA | 1 | 317 | NA |
| TCGA-NC-A5HQ-01 | TCGA-NC-A5HQ | 1 | 448 | NA | 448 | 0 | 448 | 0 | 448 | NA |
| TCGA-NC-A5HR-01 | TCGA-NC-A5HR | 0 | 1244 | 0 | 1244 | 0 | 1244 | 0 | 1244 | NA |
| TCGA-NC-A5HT-01 | TCGA-NC-A5HT | 0 | 804 | 0 | 804 | 1 | 203 | 1 | 203 | NA |
| TCGA-NK-A5CR-01 | TCGA-NK-A5CR | 0 | 2542 | 0 | 2542 | NA | NA | 0 | 2542 | NA |
| TCGA-NK-A5CT-01 | TCGA-NK-A5CT | 0 | 1997 | 0 | 1997 | NA | NA | 1 | 1990 | NA |
| TCGA-NK-A5CX-01 | TCGA-NK-A5CX | 0 | 111 | 0 | 111 | NA | NA | 0 | 111 | NA |
| TCGA-NK-A5D1-01 | TCGA-NK-A5D1 | 0 | 511 | 0 | 511 | NA | NA | 1 | 151 | NA |
| TCGA-NK-A7XE-01 | TCGA-NK-A7XE | 0 | 13 | 0 | 13 | NA | NA | 0 | 13 | NA |
| TCGA-O2-A52N-01 | TCGA-O2-A52N | 1 | 1006 | 0 | 1006 | NA | NA | 0 | 1006 | NA |
| TCGA-O2-A52Q-01 | TCGA-O2-A52Q | 1 | 113 | 1 | 113 | NA | NA | 1 | 87 | NA |
| TCGA-O2-A52S-01 | TCGA-O2-A52S | 1 | 387 | 1 | 387 | NA | NA | 1 | 246 | NA |
| TCGA-O2-A52V-01 | TCGA-O2-A52V | 1 | 1335 | 0 | 1335 | NA | NA | 1 | 690 | NA |
| TCGA-O2-A52W-01 | TCGA-O2-A52W | 1 | 261 | 0 | 261 | NA | NA | 0 | 261 | NA |
| TCGA-O2-A5IB-01 | TCGA-O2-A5IB | 1 | 340 | 1 | 340 | NA | NA | 1 | 253 | NA |
| TCGA-XC-AA0X-01 | TCGA-XC-AA0X | 1 | 6 | 0 | 6 | NA | NA | 0 | 6 | NA |
In [1283]:
survival <- survival[match(names(score),survival$sample),]
survival$score <- score
In [1284]:
survival
| sample | _PATIENT | OS | OS.time | DSS | DSS.time | DFI | DFI.time | PFI | PFI.time | Redaction | score |
|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <lgl> | <dbl> |
| TCGA-NJ-A4YP-01 | TCGA-NJ-A4YP | 0 | 50 | 0 | 50 | NA | NA | 0 | 50 | NA | -0.0002982958 |
| TCGA-18-3417-01 | TCGA-18-3417 | 1 | 1097 | 0 | 1097 | NA | NA | 0 | 1097 | NA | 0.0258459039 |
| TCGA-22-4613-01 | TCGA-22-4613 | 1 | 358 | NA | 358 | NA | NA | 0 | 358 | NA | 0.0148501111 |
| TCGA-90-7769-01 | TCGA-90-7769 | 0 | 358 | 0 | 358 | 0 | 358 | 0 | 358 | NA | 0.0244142264 |
| TCGA-62-8397-01 | TCGA-62-8397 | 0 | 1289 | 0 | 1289 | 0 | 1289 | 0 | 1289 | NA | -0.0053270352 |
| TCGA-77-A5G1-01 | TCGA-77-A5G1 | 0 | 4026 | 0 | 4026 | 0 | 4026 | 1 | 2197 | NA | 0.0278548672 |
| TCGA-86-8278-01 | TCGA-86-8278 | 0 | 944 | 0 | 944 | 1 | 29 | 1 | 29 | NA | 0.0095782341 |
| TCGA-77-A5G3-01 | TCGA-77-A5G3 | 0 | 4570 | 0 | 4570 | 0 | 4570 | 0 | 4570 | NA | 0.0218453711 |
| TCGA-05-4430-01 | TCGA-05-4430 | 0 | 761 | 0 | 761 | NA | NA | 0 | 761 | NA | 0.0150223382 |
| TCGA-44-6778-01 | TCGA-44-6778 | 0 | 1864 | 0 | 1864 | 0 | 1864 | 0 | 1864 | NA | 0.0131329978 |
| TCGA-49-AARQ-01 | TCGA-49-AARQ | 0 | 6732 | 0 | 6732 | 0 | 6732 | 0 | 6732 | NA | -0.0059755051 |
| TCGA-66-2766-01 | TCGA-66-2766 | 0 | 31 | 0 | 31 | NA | NA | 0 | 31 | NA | 0.0232599838 |
| TCGA-MP-A4TK-01 | TCGA-MP-A4TK | 1 | 582 | 1 | 582 | 1 | 397 | 1 | 397 | NA | 0.0106267321 |
| TCGA-37-4135-01 | TCGA-37-4135 | 0 | 207 | 0 | 207 | NA | NA | 0 | 207 | NA | 0.0105433144 |
| TCGA-55-6975-01 | TCGA-55-6975 | 1 | 118 | 1 | 118 | NA | NA | 1 | 118 | NA | 0.0215167339 |
| TCGA-56-8201-01 | TCGA-56-8201 | 1 | 397 | 1 | 397 | NA | NA | 1 | 397 | NA | 0.0141304397 |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0.0093524792 |
| TCGA-43-7656-01 | TCGA-43-7656 | 0 | 596 | 0 | 596 | 0 | 596 | 0 | 596 | NA | 0.0140372338 |
| TCGA-85-8481-01 | TCGA-85-8481 | 1 | 236 | 0 | 236 | NA | NA | 0 | 236 | NA | 0.0331640891 |
| TCGA-56-A4ZK-01 | TCGA-56-A4ZK | 0 | 570 | 0 | 570 | 0 | 570 | 0 | 570 | NA | 0.0208564429 |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0.0112404426 |
| TCGA-22-1011-01 | TCGA-22-1011 | 1 | 53 | 0 | 53 | NA | NA | 0 | 53 | NA | 0.0247210733 |
| TCGA-78-7160-01 | TCGA-78-7160 | 1 | 697 | NA | 697 | NA | NA | 0 | 697 | NA | 0.0063607608 |
| TCGA-77-8156-01 | TCGA-77-8156 | 0 | 1106 | 0 | 1106 | 0 | 1106 | 0 | 1106 | NA | 0.0118523309 |
| TCGA-78-8655-01 | TCGA-78-8655 | 0 | 2360 | 0 | 2360 | 0 | 2360 | 0 | 2360 | NA | 0.0052867446 |
| TCGA-43-A475-01 | TCGA-43-A475 | 0 | 296 | 0 | 296 | 0 | 296 | 0 | 296 | NA | 0.0080940485 |
| TCGA-69-8255-01 | TCGA-69-8255 | 0 | 129 | 0 | 129 | NA | NA | 0 | 129 | NA | -0.0035890406 |
| TCGA-67-3773-01 | TCGA-67-3773 | 0 | 427 | 0 | 427 | NA | NA | 0 | 427 | NA | 0.0030997943 |
| TCGA-37-3789-01 | TCGA-37-3789 | 0 | 13 | 0 | 13 | NA | NA | 0 | 13 | NA | 0.0256030702 |
| TCGA-97-8552-01 | TCGA-97-8552 | 0 | 626 | 0 | 626 | 0 | 626 | 0 | 626 | NA | 0.0048810961 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| TCGA-O2-A52S-01 | TCGA-O2-A52S | 1 | 387 | 1 | 387 | NA | NA | 1 | 246 | NA | 0.002323630 |
| TCGA-33-4586-01 | TCGA-33-4586 | 1 | 428 | NA | 428 | 1 | 169 | 1 | 169 | NA | 0.012394929 |
| TCGA-21-1077-01 | TCGA-21-1077 | 1 | 1058 | 1 | 1058 | NA | NA | 1 | 644 | NA | 0.018856090 |
| TCGA-60-2713-01 | TCGA-60-2713 | 0 | 1731 | 0 | 1731 | 1 | 1356 | 1 | 1356 | NA | 0.018597410 |
| TCGA-97-8172-01 | TCGA-97-8172 | 0 | 545 | 0 | 545 | 0 | 545 | 0 | 545 | NA | 0.006024700 |
| TCGA-L3-A524-01 | TCGA-L3-A524 | 1 | 490 | 0 | 490 | 0 | 490 | 0 | 490 | NA | 0.011965620 |
| TCGA-39-5021-01 | TCGA-39-5021 | 1 | 2086 | 1 | 2086 | 1 | 1875 | 1 | 1875 | NA | 0.014908740 |
| TCGA-85-A5B5-01 | TCGA-85-A5B5 | 0 | 111 | 0 | 111 | 0 | 111 | 0 | 111 | NA | -0.001921737 |
| TCGA-55-6981-11 | TCGA-55-6981 | 1 | 1379 | 0 | 1379 | 0 | 1379 | 0 | 1379 | NA | 0.006153550 |
| TCGA-58-A46L-01 | TCGA-58-A46L | 0 | 1723 | 0 | 1723 | 0 | 1723 | 0 | 1723 | NA | 0.013550788 |
| TCGA-77-8144-01 | TCGA-77-8144 | 0 | 833 | 0 | 833 | 0 | 833 | 0 | 833 | NA | 0.019250980 |
| TCGA-J1-A4AH-01 | TCGA-J1-A4AH | 0 | 581 | 0 | 581 | 0 | 581 | 0 | 581 | NA | 0.007312451 |
| TCGA-86-7713-01 | TCGA-86-7713 | 0 | 1157 | 0 | 1157 | 0 | 1157 | 0 | 1157 | NA | 0.008360764 |
| TCGA-78-7152-01 | TCGA-78-7152 | 1 | 1215 | 1 | 1215 | 1 | 1202 | 1 | 1202 | NA | 0.005906560 |
| TCGA-50-5946-01 | TCGA-50-5946 | 0 | 1617 | 0 | 1617 | NA | NA | 1 | 221 | NA | 0.014507656 |
| TCGA-56-7580-01 | TCGA-56-7580 | 0 | 925 | 0 | 925 | 0 | 925 | 0 | 925 | NA | 0.019822712 |
| TCGA-62-A46S-01 | TCGA-62-A46S | 1 | 1653 | 1 | 1653 | 1 | 527 | 1 | 527 | NA | -0.003521411 |
| TCGA-95-7947-01 | TCGA-95-7947 | 0 | 477 | 0 | 477 | 0 | 477 | 0 | 477 | NA | 0.022236653 |
| TCGA-J2-8192-01 | TCGA-J2-8192 | 0 | 739 | 0 | 739 | NA | NA | 1 | 482 | NA | 0.014575084 |
| TCGA-55-6987-01 | TCGA-55-6987 | 0 | 2137 | 0 | 2137 | 0 | 2137 | 0 | 2137 | NA | 0.004460157 |
| TCGA-63-A5MY-01 | TCGA-63-A5MY | 0 | 1052 | 0 | 1052 | 0 | 1052 | 0 | 1052 | NA | 0.006623062 |
| TCGA-50-6593-01 | TCGA-50-6593 | 1 | 336 | 1 | 336 | NA | NA | 1 | 266 | NA | 0.009686103 |
| TCGA-33-AASL-01 | TCGA-33-AASL | 1 | 826 | 1 | 826 | NA | NA | 1 | 149 | NA | 0.017489178 |
| TCGA-85-A512-01 | TCGA-85-A512 | 0 | 465 | 0 | 465 | 0 | 465 | 0 | 465 | NA | 0.010157524 |
| TCGA-85-8354-01 | TCGA-85-8354 | 0 | 995 | 0 | 995 | 0 | 995 | 0 | 995 | NA | 0.013494383 |
| TCGA-O2-A5IB-01 | TCGA-O2-A5IB | 1 | 340 | 1 | 340 | NA | NA | 1 | 253 | NA | 0.012459250 |
| TCGA-67-3771-01 | TCGA-67-3771 | 0 | 610 | 0 | 610 | NA | NA | 0 | 610 | NA | 0.019927787 |
| TCGA-77-7335-01 | TCGA-77-7335 | 1 | 2133 | NA | 2133 | NA | NA | 0 | 2133 | NA | 0.015740704 |
| TCGA-55-8302-01 | TCGA-55-8302 | 0 | 478 | 0 | 478 | 0 | 478 | 0 | 478 | NA | 0.016035737 |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0.013989310 |
In [1285]:
median(score)
mean(score)
0.012346634531988
0.0124252900406258
In [1286]:
survival$group<- ifelse((survival$score < 0.015), 1,2)
In [1287]:
table(survival$group)
1 2 710 419
In [1288]:
fit.surv <-Surv(survival$OS.time,survival$OS)
km<-survfit(fit.surv~1,data = survival)
km_2<- survfit(fit.surv~group,data=survival)
In [1289]:
ggsurvplot (km)
In [1290]:
ggsurvplot(km_2,pval=TRUE)
In [1291]:
fit.surv <-Surv(survival$DFI.time,survival$DFI)
km_2<- survfit(fit.surv~group,data=survival)
In [1292]:
ggsurvplot(km_2,pval=TRUE)
In [1293]:
fit.surv <-Surv(survival$PFI.time,survival$PFI)
km_2<- survfit(fit.surv~group,data=survival)
In [1294]:
ggsurvplot(km_2,pval=TRUE)
In [1295]:
survival$group<- ifelse((survival$score < 0.021), 1,2)
In [1296]:
fit.surv <-Surv(survival$PFI.time,survival$PFI)
km_2<- survfit(fit.surv~group,data=survival)
ggsurvplot(km_2,pval=TRUE)
In [1297]:
egfr_sur <- survival %>% merge(.,GDC_phenotype[! GDC_phenotype$EGFR %in% c('none',''),],by.x ='sample',by.y='sampleID',all = F)
In [1298]:
egfr_sur$group<- ifelse(egfr_sur$score < 0.02,1,2)
In [1299]:
fit.surv <-Surv(egfr_sur$OS.time,egfr_sur$OS)
km_2<- survfit(fit.surv~group,data=egfr_sur)
ggsurvplot(km_2,pval=TRUE)
In [1300]:
survival$group<- ifelse((survival$score < 0.021), 1,2)
In [1301]:
fit.surv <-Surv(survival$OS.time,survival$OS)
km_2<- survfit(fit.surv~group,data=survival)
In [1302]:
p <- ggsurvplot(km_2, main = "Survival curve",
conf.int = TRUE,# 可信区间
palette = "npg",# 支持ggsci配色,自定义颜色,brewer palettes中的配色,等
ggtheme = theme_classic(), # 支持ggplot2及其扩展包的主题
legend.title = "Score",#改变图例名称
legend.labs = c("Low", "High"),
risk.table = TRUE, # 增加risk table
#risk.table.title = "Risk set sizes",
ncensor.plot = TRUE, #增加删失事件表
surv.median.line = "hv",
pval=TRUE, #添加P值
log.rank.weights = "1", #pval.method
)
p
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?”
In [1308]:
#https://github.com/kassambara/survminer/issues/152
# add method to grid.draw
grid.draw.ggsurvplot <- function(x){
survminer:::print.ggsurvplot(x, newpage = FALSE)
}
# Remember to pass object `p`.
ggsave(
filename ='survplot_TCGA_LUSC_byScore2.pdf',
plot = p,
device = 'pdf',
#path = 'data/output',
width = 8,
height = 10
)
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?”
In [1309]:
getwd()
'/sibcb1/chenluonanlab8/miaoyuanxiang/CauFinder-master'
In [1312]:
ggsave(
filename ='survplot_TCGA_LUSC_byScore2.pdf',
plot = p,
device = 'pdf',
#path = 'data/output',
width = 8,
height = 11
)
Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?” Warning message in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : “All aesthetics have length 1, but the data has 2 rows. ℹ Did you mean to use `annotate()`?”
